Showing 1–50 of 110 results for author: Ritschel, T

Volumetric Inverse Rendering via Neural Radiative Transfer

Authors: Ntumba Elie Nsampi, Adarsh Djeacoumar, Hans-Peter Seidel, Tobias Ritschel, Thomas Leimkühler

Abstract: Volumetric inverse rendering seeks to recover the optical properties of participating media from images. Existing approaches either rely on differentiable stochastic light transport simulation, which require substantial algorithmic effort, or use simplified models that fail to capture global illumination. We propose a formulation that reconciles physically complete light transport with general-pur… ▽ More Volumetric inverse rendering seeks to recover the optical properties of participating media from images. Existing approaches either rely on differentiable stochastic light transport simulation, which require substantial algorithmic effort, or use simplified models that fail to capture global illumination. We propose a formulation that reconciles physically complete light transport with general-purpose neural optimization. The optical properties of the medium and the full light field are represented as neural fields and estimated through a joint optimization process. Global illumination is enforced via a residual objective derived from the Radiative Transfer Equation in local differential form, complemented by a volume rendering term along primary viewing rays to mitigate \rev{low-frequency} bias. We demonstrate reconstruction of spatially varying, color-resolved scattering, absorption, and phase function parameters from multi-view images. Beyond reconstruction, the same framework supports learning generative models of participating media with physical optical properties under global illumination. △ Less

Submitted 15 July, 2026; originally announced July 2026.

Uniaxial-Stress-Induced Magnetic Transitions in the Triangular-Lattice Antiferromagnet PdCrO2

Authors: Nina Stilkerich, Tobias Ritschel, Hilary M. L. Noad, Richard Waite, Dmitry Khalyavin, Kousuke Ishida, Pascal Manuel, Fabio Orlandi, Seunghyun Khim, Elena Gati, Andrew P. Mackenzie, Jochen Geck, Clifford W. Hicks

Abstract: Uniaxial stress is a promising method to tune magnetic frustration, allowing its effects to be studied in a precise way. In this work, uniaxial stress is applied to the triangular-lattice antiferromagnet PdCrO2. The Cr-Cr magnetic interaction is very sensitive to interatomic separation, so laboratory-achievable stress can induce substantial changes in magnetic structure. Results from three types o… ▽ More Uniaxial stress is a promising method to tune magnetic frustration, allowing its effects to be studied in a precise way. In this work, uniaxial stress is applied to the triangular-lattice antiferromagnet PdCrO2. The Cr-Cr magnetic interaction is very sensitive to interatomic separation, so laboratory-achievable stress can induce substantial changes in magnetic structure. Results from three types of measurement are presented: X-ray diffraction, the stress-strain relationship, and neutron diffraction. The combined data show that the elastic moduli of PdCrO2 are strongly affected by stress-induced changes in magnetic structure. A new, first-order stress-induced magnetic transition is observed, at which the lattice constant shrinks by 0.21%. The lattice stiffens dramatically across this transition: the Young's modulus increases by about 80 GPa, and the Poisson ratio falls from about 1 to about 0.4. This stiffening indicates that the magnetic order "locks," that is, becomes insensitive to lattice strain. This locking might occur because the new stress-induced magnetic order nests the Fermi surface of the Pd sheets. Other frustrated magnets, including candidate spin liquids, may show similarly strong coupling between magnetic and elastic degrees of freedom. △ Less

Submitted 8 June, 2026; originally announced June 2026.

Comments: 16 pages. To be published in Reports on Progress in Physics

An evaluation framework for sparse 4D (3D + time) imaging reconstruction via bootstrapped cross-validation

Authors: Yuhe Zhang, Zisheng Yao, Zhe Hu, Tobias Ritschel, Pablo Villanueva-Perez

Abstract: Four-dimensional (4D; 3D + time) microscopic imaging has emerged as a powerful technique for investigating dynamic phenomena in complex systems, enabling direct visualization of structural evolution in space and time. However, when pushing the limits of spatiotemporal resolution, most time-resolved imaging techniques yield inherently sparse 4D datasets. While deep learning-based reconstruction met… ▽ More Four-dimensional (4D; 3D + time) microscopic imaging has emerged as a powerful technique for investigating dynamic phenomena in complex systems, enabling direct visualization of structural evolution in space and time. However, when pushing the limits of spatiotemporal resolution, most time-resolved imaging techniques yield inherently sparse 4D datasets. While deep learning-based reconstruction methods have shown promise in reconstructing 4D from sparse spatiotemporal measurements, a practical approach for evaluating their performance in the absence of a 4D reference has, to the best of our knowledge, been lacking. Here, we present a bootstrapped cross-validation framework that estimates reconstruction performance by quantifying correlations between reconstructions generated from independently sampled subsets of the acquired data, as inspired by the 3D validation strategy in cryo-electron microscopy, where reconstructions from split datasets are compared to assess resolutions. This enables both qualitative and quantitative assessment in the absence of ground truth. We investigate two representative scenarios with sparse and ultra-sparse X-ray datasets and validate this approach using 4D-ONIX, a 4D deep-learning reconstruction method, on simulated water droplet collision experiments. The proposed approach provides a reference-free framework for performance estimation and support for better-informed experimental strategies across a wide range of ultrafast imaging applications. △ Less

Submitted 18 May, 2026; originally announced May 2026.

Uniaxial strain-driven ferroelastic domain control in LaAlO3

Authors: Matthias Roeper, Robin Buschbeck, Jakob Wetzel, Tobias Ritschel, Anna-Lena Hofmann, Vladyslav Kovtunovych, Mike N. Pionteck, Javier Taboada-Gutiérrez, Alexey B. Kuzmenko, Martina Basini, Vivek Unikandanunni, Iuliia Kiseleva, Jochen Geck, Susanne C. Kehr, Maximilian Lederer, Simone Sanna, Lukas M. Eng, Samuel D. Seddon

Abstract: Multiferroic domain walls in functional oxides exhibit properties distinct from the bulk and are increasingly exploited as active elements in nanoelectronic and photonic devices. Deterministic control of domain populations has typically remained limited to local control, or removal with temperature. Here we demonstrate continuous, reversible manipulation of the ferroelastic domain structure in sin… ▽ More Multiferroic domain walls in functional oxides exhibit properties distinct from the bulk and are increasingly exploited as active elements in nanoelectronic and photonic devices. Deterministic control of domain populations has typically remained limited to local control, or removal with temperature. Here we demonstrate continuous, reversible manipulation of the ferroelastic domain structure in single-crystal LaAlO$_3$ using in-situ uniaxial strain. Combining atomic force microscopy, X-ray diffraction, and Raman spectroscopy with first-principles calculations we map the complete microscopic evolution of the twin domain population through the strain-driven transition from the rhombohedral $R\bar{3}c$ ground state toward the predicted orthorhombic $Fmmm$ phase. Applied strains below $0.5\%$ produce pronounced surface flattening and large-scale domain reorganisation, establishing uniaxial strain as a technically accessible control parameter for ferroelastic domain engineering. These results open a route to active, real-time programming of domain architectures in LaAlO$_3$-based heterostructures, with implications for strain-tunable superconducting interfaces, nanoscale phonon-polariton optics, and ultrafast lattice control. △ Less

Submitted 30 April, 2026; originally announced April 2026.

Cross-Atlantic Research Agenda for Scalable Grid Architectures and Distributed Flexibility

Authors: Mads R. Almassalkhi, Dakota Hamilton, Hasan Giray Oral, Yury Dvorkin, Dennice Gayme, Bri-Mathias Hodge, Brian Vad Mathiesen, Jakob Stoustrup, Tobias Ritschel, Rune G. Junker, Shahab Tohidi, Razgar Ebrahimy, Henrik Madsen

Abstract: Electric power systems are rapidly evolving into deeply digital, cyber-physical infrastructures in which large fleets of distributed energy resources must be coordinated as system-level flexibility across multiple spatial and temporal scales. Despite growing distributed energy resource deployment, existing grid and market architectures lack scalable, interoperable mechanisms to reliably translate… ▽ More Electric power systems are rapidly evolving into deeply digital, cyber-physical infrastructures in which large fleets of distributed energy resources must be coordinated as system-level flexibility across multiple spatial and temporal scales. Despite growing distributed energy resource deployment, existing grid and market architectures lack scalable, interoperable mechanisms to reliably translate device-level flexibility into grid-aware services, creating risks to reliability, affordability, and resilience at high penetration. We propose that scalable and reliable coordination of distributed energy resource-based flexibility in future power systems is fundamentally an architectural problem that can be addressed through laminar cyber-physical design using minimal, standardized interoperability interfaces that link device autonomy with system-level objectives. To assess this claim, we present and discuss a layered cyber-physical systems architecture and explicate its implementation through standards-based interfaces, Flexibility Functions, hierarchical control, and case studies spanning U.S. and Danish regulatory, market, and operational contexts. Empirical evidence from New York's Grid of the Future proceedings, Danish Smart Energy Operating System pilots, and operational aggregator deployments demonstrates that such architecture enables predictable, grid-aware flexibility while preserving device autonomy, interoperability, reliability, and quality of service. These results support a cross-Atlantic research agenda centered on joint testbeds, harmonized interoperability mechanisms, and coordinated policy experiments to accelerate the deployment of resilient, scalable, and flexible clean energy systems. △ Less

Submitted 21 April, 2026; originally announced April 2026.

Journal ref: Smart Energy, Volume 22, 2026, 100236, ISSN 2666-9552

Photons x Force: Differentiable Radiation Pressure Modeling

Authors: Charles Constant, Elizabeth Bates, Santosh Bhattarai, Marek Ziebart, Tobias Ritschel

Abstract: We propose a system to optimize parametric designs subject to radiation pressure, \ie the effect of light on the motion of objects. This is most relevant in the design of spacecraft, where radiation pressure presents the dominant non-conservative forcing mechanism, which is the case beyond approximately 800 km altitude. Despite its importance, the high computational cost of high-fidelity radiation… ▽ More We propose a system to optimize parametric designs subject to radiation pressure, \ie the effect of light on the motion of objects. This is most relevant in the design of spacecraft, where radiation pressure presents the dominant non-conservative forcing mechanism, which is the case beyond approximately 800 km altitude. Despite its importance, the high computational cost of high-fidelity radiation pressure modeling has limited its use in large-scale spacecraft design, optimization, and space situational awareness applications. We enable this by offering three innovations in the simulation, in representation and in optimization: First, a practical computer graphics-inspired Monte-Carlo (MC) simulation of radiation pressure. The simulation is highly parallel, uses importance sampling and next-event estimation to reduce variance and allows simulating an entire family of designs instead of a single spacecraft as in previous work. Second, we introduce neural networks as a representation of forces from design parameters. This neural proxy model, learned from simulations, is inherently differentiable and can query forces orders of magnitude faster than a full MC simulation. Third, and finally, we demonstrate optimizing inverse radiation pressure designs, such as finding geometry, material or operation parameters that minimizes travel time, maximizes proximity given a desired end-point, minimize thruster fuel, trains mission control policies or allocated compute budget in extraterrestrial compute. △ Less

Submitted 6 May, 2026; v1 submitted 11 February, 2026; originally announced February 2026.

Comments: Camera-ready version. Accepted to ACM Transactions on Graphics 45(4). (SIGGRAPH 2026), article 82. 17 pages, 19 figures

ACM Class: I.3.7; I.3.5; I.6.3; I.2.6

Journal ref: ACM Transactions on Graphics, Vol. 45, No. 4, Article 82 (July 2026)

Learning Neural Antiderivatives

Authors: Fizza Rubab, Ntumba Elie Nsampi, Martin Balint, Felix Mujkanovic, Hans-Peter Seidel, Tobias Ritschel, Thomas Leimkühler

Abstract: Neural fields offer continuous, learnable representations that extend beyond traditional discrete formats in visual computing. We study the problem of learning neural representations of repeated antiderivatives directly from a function, a continuous analogue of summed-area tables. Although widely used in discrete domains, such cumulative schemes rely on grids, which prevents their applicability in… ▽ More Neural fields offer continuous, learnable representations that extend beyond traditional discrete formats in visual computing. We study the problem of learning neural representations of repeated antiderivatives directly from a function, a continuous analogue of summed-area tables. Although widely used in discrete domains, such cumulative schemes rely on grids, which prevents their applicability in continuous neural contexts. We introduce and analyze a range of neural methods for repeated integration, including both adaptations of prior work and novel designs. Our evaluation spans multiple input dimensionalities and integration orders, assessing both reconstruction quality and performance in downstream tasks such as filtering and rendering. These results enable integrating classical cumulative operators into modern neural systems and offer insights into learning tasks involving differential and integral operators. △ Less

Submitted 22 September, 2025; originally announced September 2025.

Comparing Model-based Control Strategies for a Quadruple Tank System: Decentralized PID, LMPC, and NMPC

Authors: Anders H. D. Christensen, Tobias K. S. Ritschel, Jan Lorenz Svensen, Steen Hørsholt, Jakob Kjøbsted Huusom, John Bagterp Jørgensen

Abstract: This paper compares the performance of a decentralized proportional-integral-derivative (PID) controller, a linear model predictive controller (LMPC), and a nonlinear model predictive controller (NMPC) applied to a quadruple tank system (QTS). We present experimental data from a physical setup of the QTS as well as simulation results. The QTS is modeled as a stochastic nonlinear continuous-discret… ▽ More This paper compares the performance of a decentralized proportional-integral-derivative (PID) controller, a linear model predictive controller (LMPC), and a nonlinear model predictive controller (NMPC) applied to a quadruple tank system (QTS). We present experimental data from a physical setup of the QTS as well as simulation results. The QTS is modeled as a stochastic nonlinear continuous-discrete-time system, with parameters estimated using a maximum-likelihood prediction-error-method (ML-PEM). The NMPC applies the stochastic nonlinear continuous-discrete-time model, while the LMPC uses a linearized version of the same model. We tune the decentralized PID controller using the simple internal model control (SIMC) rules. The SIMC rules require transfer functions of the process, and we obtain these from the linearized model. We compare the controller performances based on systematic tests using both the physical setup and the simulated QTS. We measure the performance in terms of tracking errors and rate of movement in the manipulated variables. The LMPC and the NMPC perform better than the decentralized PID control system for tracking pre-announced time-varying setpoints. For disturbance rejection, the MPCs perform only slightly better than the decentralized PID controller. The primary advantage of the MPCs is their ability to use the information of future setpoints. We demonstrate this by providing simulation results of the MPCs with and without such information. Finally, the NMPC achieves slightly improved tracking errors compared to the LMPC but at the expense of having a higher input rate of movement. △ Less

Submitted 14 September, 2025; originally announced September 2025.

Comments: 18 pages, 12 figures

MSC Class: 49

Strain-control of electronic superlattice domains in CsV$_{3}$Sb$_{5}$

Authors: Elaheh Sadrollahi, João Almeida Mendes, Nina Stilkerich, Avdhesh Kumar Sharma, Chandra Shekhar, Claudia Felser, Tobias Ritschel, Jochen Geck

Abstract: The kagome metals AV$_{3}$Sb$_{5}$ (A = K, Rb, Cs) provide a unique platform to investigate the physics of interacting electrons, a central challenge in condensed matter physics. A key obstacle in unraveling their correlated behavior is to determine which structural and electronic degrees of freedom are involved and how they couple. Here we address this important issue with a novel approach, namel… ▽ More The kagome metals AV$_{3}$Sb$_{5}$ (A = K, Rb, Cs) provide a unique platform to investigate the physics of interacting electrons, a central challenge in condensed matter physics. A key obstacle in unraveling their correlated behavior is to determine which structural and electronic degrees of freedom are involved and how they couple. Here we address this important issue with a novel approach, namely by exploring the strain dependence of electronic superlattices in CsV$_{3}$Sb$_{5}$. Using high-resolution x-ray diffraction, we track the detwinning of the $2\times 2\times 4$ electronic crystal and uncover a gigantic strain response of its domains. We further show that the detwinned $2\times 2\times 4$ phase exhibits an intrinsic 2$\textbf{q}$-modulation and strong mode coupling. Density functional theory reveals that the structural $2\times 2\times 4$ modulation couples strongly to the V $3d$-orbitals, naturally explaining its pronounced strain response. In contrast, the $2\times 2\times 2$ phase at lower temperatures remains essentially unaffected by small uniaxial strain. This dichotomy points to fundamental differences in the symmetry breaking and stabilization mechanisms of the two electronic orders. More specifically, our results provide evidence for the active role of orbital degrees of freedom, which can realize distinct, complex ordering patterns driven by competing interactions. △ Less

Submitted 6 September, 2025; originally announced September 2025.

Comments: 11 pages, 8 figures,1 table

Physics-informed 4D X-ray image reconstruction from ultra-sparse spatiotemporal data

Authors: Zisheng Yao, Yuhe Zhang, Zhe Hu, Robert Klöfkorn, Tobias Ritschel, Pablo Villanueva-Perez

Abstract: The unprecedented X-ray flux density provided by modern X-ray sources offers new spatiotemporal possibilities for X-ray imaging of fast dynamic processes. Approaches to exploit such possibilities often result in either i) a limited number of projections or spatial information due to limited scanning speed, as in time-resolved tomography, or ii) a limited number of time points, as in stroboscopic i… ▽ More The unprecedented X-ray flux density provided by modern X-ray sources offers new spatiotemporal possibilities for X-ray imaging of fast dynamic processes. Approaches to exploit such possibilities often result in either i) a limited number of projections or spatial information due to limited scanning speed, as in time-resolved tomography, or ii) a limited number of time points, as in stroboscopic imaging, making the reconstruction problem ill-posed and unlikely to be solved by classical reconstruction approaches. 4D reconstruction from such data requires sample priors, which can be included via deep learning (DL). State-of-the-art 4D reconstruction methods for X-ray imaging combine the power of AI and the physics of X-ray propagation to tackle the challenge of sparse views. However, most approaches do not constrain the physics of the studied process, i.e., a full physical model. Here we present 4D physics-informed optimized neural implicit X-ray imaging (4D-PIONIX), a novel physics-informed 4D X-ray image reconstruction method combining the full physical model and a state-of-the-art DL-based reconstruction method for 4D X-ray imaging from sparse views. We demonstrate and evaluate the potential of our approach by retrieving 4D information from ultra-sparse spatiotemporal acquisitions of simulated binary droplet collisions, a relevant fluid dynamic process. We envision that this work will open new spatiotemporal possibilities for various 4D X-ray imaging modalities, such as time-resolved X-ray tomography and more novel sparse acquisition approaches like X-ray multi-projection imaging, which will pave the way for investigations of various rapid 4D dynamics, such as fluid dynamics and composite testing. △ Less

Submitted 4 April, 2025; originally announced April 2025.

Generative Video Bi-flow

Authors: Chen Liu, Tobias Ritschel

Abstract: We propose a novel generative video model to robustly learn temporal change as a neural Ordinary Differential Equation (ODE) flow with a bilinear objective which combines two aspects: The first is to map from the past into future video frames directly. Previous work has mapped the noise to new frames, a more computationally expensive process. Unfortunately, starting from the previous frame, instea… ▽ More We propose a novel generative video model to robustly learn temporal change as a neural Ordinary Differential Equation (ODE) flow with a bilinear objective which combines two aspects: The first is to map from the past into future video frames directly. Previous work has mapped the noise to new frames, a more computationally expensive process. Unfortunately, starting from the previous frame, instead of noise, is more prone to drifting errors. Hence, second, we additionally learn how to remove the accumulated errors as the joint objective by adding noise during training. We demonstrate unconditional video generation in a streaming manner for various video datasets, all at competitive quality compared to a conditional diffusion baseline but with higher speed, i.e., fewer ODE solver steps. △ Less

Submitted 8 August, 2025; v1 submitted 8 March, 2025; originally announced March 2025.

Comments: ICCV 2025. Project Page at https://ryushinn.github.io/ode-video

Blind Augmentation: Calibration-free Camera Distortion Model Estimation for Real-time Mixed-reality Consistency

Authors: Siddhant Prakash, David R. Walton, Rafael K. dos Anjos, Anthony Steed, Tobias Ritschel

Abstract: Real camera footage is subject to noise, motion blur (MB) and depth of field (DoF). In some applications these might be considered distortions to be removed, but in others it is important to model them because it would be ineffective, or interfere with an aesthetic choice, to simply remove them. In augmented reality applications where virtual content is composed into a live video feed, we can mode… ▽ More Real camera footage is subject to noise, motion blur (MB) and depth of field (DoF). In some applications these might be considered distortions to be removed, but in others it is important to model them because it would be ineffective, or interfere with an aesthetic choice, to simply remove them. In augmented reality applications where virtual content is composed into a live video feed, we can model noise, MB and DoF to make the virtual content visually consistent with the video. Existing methods for this typically suffer two main limitations. First, they require a camera calibration step to relate a known calibration target to the specific cameras response. Second, existing work require methods that can be (differentiably) tuned to the calibration, such as slow and specialized neural networks. We propose a method which estimates parameters for noise, MB and DoF instantly, which allows using off-the-shelf real-time simulation methods from e.g., a game engine in compositing augmented content. Our main idea is to unlock both features by showing how to use modern computer vision methods that can remove noise, MB and DoF from the video stream, essentially providing self-calibration. This allows to auto-tune any black-box real-time noise+MB+DoF method to deliver fast and high-fidelity augmentation consistency. △ Less

Submitted 3 March, 2025; originally announced March 2025.

Comments: To appear in IEEE Transactions on Visualization and Computer Graphics (IEEEVR 2025). Project page can be found at https://prakashsidd18.github.io/projects/blind_augmentation/

On Erlang ODE approximations of differential equations with distributed time delays

Authors: Tobias K. S. Ritschel

Abstract: In this paper, we propose a general approach for approximate simulation and analysis of delay differential equations (DDEs) with distributed time delays based on methods for ordinary differential equations (ODEs). The key innovation is that we 1) propose an Erlang mixture approximation of the kernel in the DDEs and 2) use the linear chain trick to transform the resulting approximate DDEs to ODEs.… ▽ More In this paper, we propose a general approach for approximate simulation and analysis of delay differential equations (DDEs) with distributed time delays based on methods for ordinary differential equations (ODEs). The key innovation is that we 1) propose an Erlang mixture approximation of the kernel in the DDEs and 2) use the linear chain trick to transform the resulting approximate DDEs to ODEs. We refer to this as the Erlang ODE approximation of the DDEs, and we prove that the Erlang mixture approximation converges for continuous and bounded kernels if the number of terms increases sufficiently fast. Furthermore, we show that if the kernel is also exponentially bounded, the Erlang ODE approximation can be used to assess the stability of the steady states of the original DDEs and that the solution to the ODE approximation converges. Additionally, we propose an approach based on bisection and least-squares estimation for determining optimal parameter values in the approximation. Finally, we present numerical examples that demonstrate the accuracy and convergence rates of the approximations and the efficacy of the proposed approach for bifurcation analysis and Monte Carlo simulation. The numerical examples involve a modified logistic equation, chemotherapy-induced myelosuppression, and a point reactor kinetics model of a molten salt nuclear fission reactor. △ Less

Submitted 19 July, 2026; v1 submitted 18 February, 2025; originally announced February 2025.

MSC Class: 37M05; 39B99; 41A30; 65D15; 65P99; 65Q20

Stochastic Gradient Estimation for Higher-order Differentiable Rendering

Authors: Zican Wang, Michael Fischer, Tobias Ritschel

Abstract: We derive methods to compute higher order differentials (Hessians and Hessian-vector products) of the rendering operator. Our approach is based on importance sampling of a convolution that represents the differentials of rendering parameters and shows to be applicable to both rasterization and path tracing. We further suggest an aggregate sampling strategy to importance-sample multiple dimensions… ▽ More We derive methods to compute higher order differentials (Hessians and Hessian-vector products) of the rendering operator. Our approach is based on importance sampling of a convolution that represents the differentials of rendering parameters and shows to be applicable to both rasterization and path tracing. We further suggest an aggregate sampling strategy to importance-sample multiple dimensions of one convolution kernel simultaneously. We demonstrate that this information improves convergence when used in higher-order optimizers such as Newton or Conjugate Gradient relative to a gradient descent baseline in several inverse rendering tasks. △ Less

Submitted 6 August, 2025; v1 submitted 4 December, 2024; originally announced December 2024.

SAMa: Material-aware 3D Selection and Segmentation

Authors: Michael Fischer, Iliyan Georgiev, Thibault Groueix, Vladimir G. Kim, Tobias Ritschel, Valentin Deschaintre

Abstract: Decomposing 3D assets into material parts is a common task for artists, yet remains a highly manual process. In this work, we introduce Select Any Material (SAMa), a material selection approach for in-the-wild objects in arbitrary 3D representations. Building on SAM2's video prior, we construct a material-centric video dataset that extends it to the material domain. We propose an efficient way to… ▽ More Decomposing 3D assets into material parts is a common task for artists, yet remains a highly manual process. In this work, we introduce Select Any Material (SAMa), a material selection approach for in-the-wild objects in arbitrary 3D representations. Building on SAM2's video prior, we construct a material-centric video dataset that extends it to the material domain. We propose an efficient way to lift the model's 2D predictions to 3D by projecting each view into an intermediary 3D point cloud using depth. Nearest-neighbor lookups between any 3D representation and this similarity point cloud allow us to efficiently reconstruct accurate selection masks over objects' surfaces that can be inspected from any view. Our method is multiview-consistent by design, alleviating the need for costly per-asset optimization, and performs optimization-free selection in seconds. SAMa outperforms several strong baselines in selection accuracy and multiview consistency and enables various compelling applications, such as replacing the diffuse-textured materials on a text-to-3D output with PBR materials or selecting and editing materials on NeRFs and 3DGS captures. △ Less

Submitted 20 February, 2026; v1 submitted 28 November, 2024; originally announced November 2024.

Comments: Project Page: https://mfischer-ucl.github.io/sama

Numerical optimal control for distributed delay differential equations: A simultaneous approach based on linearization of the delayed variables

Authors: Tobias K. S. Ritschel

Abstract: Time delays are ubiquitous in industrial processes, and they must be accounted for when designing control algorithms because they have a significant effect on the process dynamics. Therefore, in this work, we propose a simultaneous approach for numerical optimal control of delay differential equations with distributed time delays. Specifically, we linearize the delayed variables around the current… ▽ More Time delays are ubiquitous in industrial processes, and they must be accounted for when designing control algorithms because they have a significant effect on the process dynamics. Therefore, in this work, we propose a simultaneous approach for numerical optimal control of delay differential equations with distributed time delays. Specifically, we linearize the delayed variables around the current time, and we discretize the resulting implicit differential equations using Euler's implicit method. Furthermore, we transcribe the infinite-dimensional optimal control problem into a finite-dimensional nonlinear program, which we solve using Matlab's fmincon. Finally, we demonstrate the efficacy of the approach using a numerical example involving a molten salt nuclear fission reactor. △ Less

Submitted 19 October, 2024; originally announced October 2024.

Comments: 6 pages, 3 figures, 1 table

Neural Differential Appearance Equations

Authors: Chen Liu, Tobias Ritschel

Abstract: We propose a method to reproduce dynamic appearance textures with space-stationary but time-varying visual statistics. While most previous work decomposes dynamic textures into static appearance and motion, we focus on dynamic appearance that results not from motion but variations of fundamental properties, such as rusting, decaying, melting, and weathering. To this end, we adopt the neural ordina… ▽ More We propose a method to reproduce dynamic appearance textures with space-stationary but time-varying visual statistics. While most previous work decomposes dynamic textures into static appearance and motion, we focus on dynamic appearance that results not from motion but variations of fundamental properties, such as rusting, decaying, melting, and weathering. To this end, we adopt the neural ordinary differential equation (ODE) to learn the underlying dynamics of appearance from a target exemplar. We simulate the ODE in two phases. At the "warm-up" phase, the ODE diffuses a random noise to an initial state. We then constrain the further evolution of this ODE to replicate the evolution of visual feature statistics in the exemplar during the generation phase. The particular innovation of this work is the neural ODE achieving both denoising and evolution for dynamics synthesis, with a proposed temporal training scheme. We study both relightable (BRDF) and non-relightable (RGB) appearance models. For both we introduce new pilot datasets, allowing, for the first time, to study such phenomena: For RGB we provide 22 dynamic textures acquired from free online sources; For BRDFs, we further acquire a dataset of 21 flash-lit videos of time-varying materials, enabled by a simple-to-construct setup. Our experiments show that our method consistently yields realistic and coherent results, whereas prior works falter under pronounced temporal appearance variations. A user study confirms our approach is preferred to previous work for such exemplars. △ Less

Submitted 10 January, 2025; v1 submitted 23 September, 2024; originally announced October 2024.

Comments: SIGGRAPH Asia 2024 Journal Track. Project page at https://ryushinn.github.io/ode-appearance

Numerical optimal control for delay differential equations: A simultaneous approach based on linearization of the delayed state

Authors: Tobias K. S. Ritschel, Søren Stange

Abstract: Time delays are ubiquitous in industry, and they must be accounted for when designing control strategies. However, numerical optimal control (NOC) of delay differential equations (DDEs) is challenging because it requires specialized discretization methods and the time delays may depend on the manipulated inputs or state variables. Therefore, in this work, we propose to linearize the delayed states… ▽ More Time delays are ubiquitous in industry, and they must be accounted for when designing control strategies. However, numerical optimal control (NOC) of delay differential equations (DDEs) is challenging because it requires specialized discretization methods and the time delays may depend on the manipulated inputs or state variables. Therefore, in this work, we propose to linearize the delayed states around the current time. This results in a set of implicit differential equations, and we compare the steady states and the corresponding stability criteria of the DDEs and the approximate system. Furthermore, we propose a simultaneous approach for NOC of DDEs based on the linearization, and we discretize the approximate system using Euler's implicit method. Finally, we present a numerical example involving a molten salt nuclear fission reactor. △ Less

Submitted 19 October, 2024; v1 submitted 3 October, 2024; originally announced October 2024.

Comments: 6 pages, 4 figures, submitted to a conference

Optimal price signal generation for demand-side energy management

Authors: Seyed Shahabaldin Tohidi, Henrik Madsen, Davide Calì, Tobias K. S. Ritschel

Abstract: Renewable Energy Sources play a key role in smart energy systems. To achieve 100% renewable energy, utilizing the flexibility potential on the demand side becomes the cost-efficient option to balance the grid. However, it is not trivial to exploit these available capacities and flexibility options profitably. The amount of available flexibility is a complex and time-varying function of the price s… ▽ More Renewable Energy Sources play a key role in smart energy systems. To achieve 100% renewable energy, utilizing the flexibility potential on the demand side becomes the cost-efficient option to balance the grid. However, it is not trivial to exploit these available capacities and flexibility options profitably. The amount of available flexibility is a complex and time-varying function of the price signal and weather forecasts. In this work, we use a Flexibility Function to represent the relationship between the price signal and the demand and investigate optimization problems for the price signal computation. Consequently, this study considers the higher and lower levels in the hierarchy from the markets to appliances, households, and districts. This paper investigates optimal price generation via the Flexibility Function and studies its employment in controller design for demand-side management, its capability to provide ancillary services for balancing throughout the Smart Energy Operating System, and its effect on the physical level performance. Sequential and simultaneous approaches for computing the price signal, along with various cost functions are analyzed and compared. Simulation results demonstrate the generated price/penalty signal and its employment in a model predictive controller. △ Less

Submitted 31 July, 2024; originally announced July 2024.

Stability analysis of nonlinear stochastic flexibility function in smart energy systems

Authors: Seyed Shahabaldin Tohidi, Tobias K. S. Ritschel, Georgios Tsaousoglou, Uffe Høgsbro Thygesen, Henrik Madsen

Abstract: Demand-side management provides a great potential for improving the efficiency and reliability of energy systems. This requires a mechanism to connect the market level and the demand side. The flexibility function is a novel approach that bridges the gap between the markets and the dynamics of physical assets at the lower levels of the energy systems and activates demand-side flexibility with the… ▽ More Demand-side management provides a great potential for improving the efficiency and reliability of energy systems. This requires a mechanism to connect the market level and the demand side. The flexibility function is a novel approach that bridges the gap between the markets and the dynamics of physical assets at the lower levels of the energy systems and activates demand-side flexibility with the purpose of decision-making as well as for offering a new framework for balancing and grid services. Employing this function as a key for many decision-making and control algorithms reveals that a mathematically rigorous stability analysis is required for it. In this paper, we investigate the stability properties of two nonlinear flexibility functions, as a dynamic mapping between electricity price and power consumption. Specifically, we analyze the stability of a deterministic flexibility function and an Itô stochastic flexibility function. Simulation results are also provided to demonstrate the dynamics of the flexibility functions and to show that the analytical results hold. △ Less

Submitted 23 May, 2024; originally announced May 2024.

Bracket Diffusion: HDR Image Generation by Consistent LDR Denoising

Authors: Mojtaba Bemana, Thomas Leimkühler, Karol Myszkowski, Hans-Peter Seidel, Tobias Ritschel

Abstract: We demonstrate generating HDR images using the concerted action of multiple black-box, pre-trained LDR image diffusion models. Relying on a pre-trained LDR generative diffusion models is vital as, first, there is no sufficiently large HDR image dataset available to re-train them, and, second, even if it was, re-training such models is impossible for most compute budgets. Instead, we seek inspirati… ▽ More We demonstrate generating HDR images using the concerted action of multiple black-box, pre-trained LDR image diffusion models. Relying on a pre-trained LDR generative diffusion models is vital as, first, there is no sufficiently large HDR image dataset available to re-train them, and, second, even if it was, re-training such models is impossible for most compute budgets. Instead, we seek inspiration from the HDR image capture literature that traditionally fuses sets of LDR images, called "exposure brackets'', to produce a single HDR image. We operate multiple denoising processes to generate multiple LDR brackets that together form a valid HDR result. The key to making this work is to introduce a consistency term into the diffusion process to couple the brackets such that they agree across the exposure range they share while accounting for possible differences due to the quantization error. We demonstrate state-of-the-art unconditional and conditional or restoration-type (LDR2HDR) generative modeling results, yet in HDR. △ Less

Submitted 18 March, 2025; v1 submitted 23 May, 2024; originally announced May 2024.

Comments: 11 pages, 14 figures, Accepted to Eurographics 2025, see https://bracketdiffusion.mpi-inf.mpg.de

An algorithm for distributed time delay identification based on a mixed Erlang kernel approximation and the linear chain trick

Authors: Tobias K. S. Ritschel, John Wyller

Abstract: Time delays are ubiquitous in industry and nature, and they significantly affect both transient dynamics and stability properties. Consequently, it is often necessary to identify and account for the delays when, e.g., designing a model-based control strategy. However, identifying delays in differential equations is not straightforward and requires specialized methods. Therefore, we propose an algo… ▽ More Time delays are ubiquitous in industry and nature, and they significantly affect both transient dynamics and stability properties. Consequently, it is often necessary to identify and account for the delays when, e.g., designing a model-based control strategy. However, identifying delays in differential equations is not straightforward and requires specialized methods. Therefore, we propose an algorithm for identifying distributed delays in delay differential equations (DDEs) that only involves simulation of ordinary differential equations (ODEs). Specifically, we 1) approximate the kernel in the DDEs (also called the memory function) by the probability density function of a mixed Erlang distribution and 2) use the linear chain trick (LCT) to transform the resulting DDEs into ODEs. Finally, the parameters in the kernel approximation are estimated as the solution to a dynamical least-squares problem, and we use a single-shooting approach to approximate this solution. We demonstrate the efficacy of the algorithm using numerical examples that involve the logistic equation and a point reactor kinetics model of a molten salt nuclear fission reactor. △ Less

Submitted 12 May, 2024; originally announced May 2024.

Pressure-dependent Electronic Superlattice in the Kagome-Superconductor CsV$_3$Sb$_5$

Authors: F. Stier, A. -A. Haghighirad, G. Garbarino, S. Mishra, N. Stilkerich, D. Chen, C. Shekhar, T. Lacmann, C. Felser, T. Ritschel, J. Geck, M. Le Tacon

Abstract: We present a high-resolution single crystal x-ray diffraction study of kagome-superconductor \cvs, exploring its response to variations in pressure and temperature. We discover that at low temperatures, the structural modulations of the electronic superlattice, commonly associated with charge-density-wave order, undergo a transformation around $p \sim$ 0.7 GPa from the familiar $2\times2$ pattern… ▽ More We present a high-resolution single crystal x-ray diffraction study of kagome-superconductor \cvs, exploring its response to variations in pressure and temperature. We discover that at low temperatures, the structural modulations of the electronic superlattice, commonly associated with charge-density-wave order, undergo a transformation around $p \sim$ 0.7 GPa from the familiar $2\times2$ pattern to a long-range-ordered modulation at wavevector $q=(0, 3/8, 1/2)$. Our observations align with inferred changes in the CDW pattern from prior transport and nuclear-magnetic-resonance studies, providing new insights into these transitions. Interestingly, the pressure-induced variations in the electronic superlattice correlate with two peaks in the superconducting transition temperature as pressure changes, hinting that fluctuations within the electronic superlattice could be key to stabilizing superconductivity. However, our findings contrast with the minimal pressure dependency anticipated by ab initio calculations of the electronic structure. They also challenge prevailing scenarios based on a Peierls-like nesting mechanism involving van Hove singularities. △ Less

Submitted 7 November, 2024; v1 submitted 23 April, 2024; originally announced April 2024.

Comments: Revised version, to appear in PRL

Journal ref: Physical Review Letters 133 (23), 236503 (2024)

NeRF Analogies: Example-Based Visual Attribute Transfer for NeRFs

Authors: Michael Fischer, Zhengqin Li, Thu Nguyen-Phuoc, Aljaz Bozic, Zhao Dong, Carl Marshall, Tobias Ritschel

Abstract: A Neural Radiance Field (NeRF) encodes the specific relation of 3D geometry and appearance of a scene. We here ask the question whether we can transfer the appearance from a source NeRF onto a target 3D geometry in a semantically meaningful way, such that the resulting new NeRF retains the target geometry but has an appearance that is an analogy to the source NeRF. To this end, we generalize class… ▽ More A Neural Radiance Field (NeRF) encodes the specific relation of 3D geometry and appearance of a scene. We here ask the question whether we can transfer the appearance from a source NeRF onto a target 3D geometry in a semantically meaningful way, such that the resulting new NeRF retains the target geometry but has an appearance that is an analogy to the source NeRF. To this end, we generalize classic image analogies from 2D images to NeRFs. We leverage correspondence transfer along semantic affinity that is driven by semantic features from large, pre-trained 2D image models to achieve multi-view consistent appearance transfer. Our method allows exploring the mix-and-match product space of 3D geometry and appearance. We show that our method outperforms traditional stylization-based methods and that a large majority of users prefer our method over several typical baselines. △ Less

Submitted 13 February, 2024; originally announced February 2024.

Comments: Project page: https://mfischer-ucl.github.io/nerf_analogies/

4D-ONIX: A deep learning approach for reconstructing 3D movies from sparse X-ray projections

Authors: Yuhe Zhang, Zisheng Yao, Robert Klöfkorn, Tobias Ritschel, Pablo Villanueva-Perez

Abstract: The X-ray flux provided by X-ray free-electron lasers and storage rings offers new spatiotemporal possibilities to study in-situ and operando dynamics, even using single pulses of such facilities. X-ray Multi-Projection Imaging (XMPI) is a novel technique that enables volumetric information using single pulses of such facilities and avoids centrifugal forces induced by state-of-the-art time-resolv… ▽ More The X-ray flux provided by X-ray free-electron lasers and storage rings offers new spatiotemporal possibilities to study in-situ and operando dynamics, even using single pulses of such facilities. X-ray Multi-Projection Imaging (XMPI) is a novel technique that enables volumetric information using single pulses of such facilities and avoids centrifugal forces induced by state-of-the-art time-resolved 3D methods such as time-resolved tomography. As a result, XMPI offers the potential to acquire 3D movies (4D) at least three orders of magnitude faster than current methods. However, it is exceptionally challenging to reconstruct 4D from highly sparse projections as acquired by XMPI with current algorithms. Here, we present 4D-ONIX, a Deep Learning (DL)-based approach that learns to reconstruct 3D movies (4D) from an extremely limited number of projections. It combines the computational physical model of X-ray interaction with matter and state-of-the-art DL methods. We demonstrate the potential of 4D-ONIX to generate high-quality 4D by generalizing over multiple experiments with only two to three projections per timestamp for binary droplet collisions and additive manufacturing. We envision that 4D-ONIX will become an enabling tool for 4D analysis, offering new spatiotemporal resolutions to study processes not possible before. △ Less

Submitted 8 February, 2025; v1 submitted 17 January, 2024; originally announced January 2024.

Adaptive flexibility function in smart energy systems: A linearized price-demand mapping approach

Authors: Seyed Shahabaldin Tohidi, Henrik Madsen, Georgios Tsaousoglou, Tobias K. S. Ritschel

Abstract: This paper proposes an adaptive mechanism for price signal generation using a piecewise linear approximation of a flexibility function with unknown parameters. In this adaptive approach, the price signal is parameterized and the parameters are changed adaptively such that the output of the flexibility function follows the reference demand signal provided by the involved aggregator. This is guarant… ▽ More This paper proposes an adaptive mechanism for price signal generation using a piecewise linear approximation of a flexibility function with unknown parameters. In this adaptive approach, the price signal is parameterized and the parameters are changed adaptively such that the output of the flexibility function follows the reference demand signal provided by the involved aggregator. This is guaranteed using the Lyapunov stability theorem. The proposed method does not require an estimation algorithm for unknown parameters, that eliminates the need for persistency of excitation of signals, and consequently, simplifies offering the flexibility services. Furthermore, boundedness of the price signal is ensured using a projection algorithm in the adaptive system. We present simulation results that demonstrate the price generation results using the proposed approaches. △ Less

Submitted 15 February, 2024; v1 submitted 6 December, 2023; originally announced December 2023.

Neural Bounding

Authors: Stephanie Wenxin Liu, Michael Fischer, Paul D. Yoo, Tobias Ritschel

Abstract: Bounding volumes are an established concept in computer graphics and vision tasks but have seen little change since their early inception. In this work, we study the use of neural networks as bounding volumes. Our key observation is that bounding, which so far has primarily been considered a problem of computational geometry, can be redefined as a problem of learning to classify space into free or… ▽ More Bounding volumes are an established concept in computer graphics and vision tasks but have seen little change since their early inception. In this work, we study the use of neural networks as bounding volumes. Our key observation is that bounding, which so far has primarily been considered a problem of computational geometry, can be redefined as a problem of learning to classify space into free or occupied. This learning-based approach is particularly advantageous in high-dimensional spaces, such as animated scenes with complex queries, where neural networks are known to excel. However, unlocking neural bounding requires a twist: allowing -- but also limiting -- false positives, while ensuring that the number of false negatives is strictly zero. We enable such tight and conservative results using a dynamically-weighted asymmetric loss function. Our results show that our neural bounding produces up to an order of magnitude fewer false positives than traditional methods. In addition, we propose an extension of our bounding method using early exits that accelerates query speeds by 25%. We also demonstrate that our approach is applicable to non-deep learning models that train within seconds. Our project page is at: https://wenxin-liu.github.io/neural_bounding/. △ Less

Submitted 24 May, 2024; v1 submitted 10 October, 2023; originally announced October 2023.

Comments: Published in Proc. of SIGGRAPH, 2024

Patternshop: Editing Point Patterns by Image Manipulation

Authors: Xingchang Huang, Tobias Ritschel, Hans-Peter Seidel, Pooran Memari, Gurprit Singh

Abstract: Point patterns are characterized by their density and correlation. While spatial variation of density is well-understood, analysis and synthesis of spatially-varying correlation is an open challenge. No tools are available to intuitively edit such point patterns, primarily due to the lack of a compact representation for spatially varying correlation. We propose a low-dimensional perceptual embeddi… ▽ More Point patterns are characterized by their density and correlation. While spatial variation of density is well-understood, analysis and synthesis of spatially-varying correlation is an open challenge. No tools are available to intuitively edit such point patterns, primarily due to the lack of a compact representation for spatially varying correlation. We propose a low-dimensional perceptual embedding for point correlations. This embedding can map point patterns to common three-channel raster images, enabling manipulation with off-the-shelf image editing software. To synthesize back point patterns, we propose a novel edge-aware objective that carefully handles sharp variations in density and correlation. The resulting framework allows intuitive and backward-compatible manipulation of point patterns, such as recoloring, relighting to even texture synthesis that have not been available to 2D point pattern design before. Effectiveness of our approach is tested in several user experiments. △ Less

Submitted 3 September, 2023; v1 submitted 21 August, 2023; originally announced August 2023.

Journal ref: ACM Transactions on Graphics 2023 Volume 42 Issue 4 Article No.: 53

Zero Grads: Learning Local Surrogate Losses for Non-Differentiable Graphics

Authors: Michael Fischer, Tobias Ritschel

Abstract: Gradient-based optimization is now ubiquitous across graphics, but unfortunately can not be applied to problems with undefined or zero gradients. To circumvent this issue, the loss function can be manually replaced by a ``surrogate'' that has similar minima but is differentiable. Our proposed framework, ZeroGrads, automates this process by learning a neural approximation of the objective function,… ▽ More Gradient-based optimization is now ubiquitous across graphics, but unfortunately can not be applied to problems with undefined or zero gradients. To circumvent this issue, the loss function can be manually replaced by a ``surrogate'' that has similar minima but is differentiable. Our proposed framework, ZeroGrads, automates this process by learning a neural approximation of the objective function, which in turn can be used to differentiate through arbitrary black-box graphics pipelines. We train the surrogate on an actively smoothed version of the objective and encourage locality, focusing the surrogate's capacity on what matters at the current training episode. The fitting is performed online, alongside the parameter optimization, and self-supervised, without pre-computed data or pre-trained models. As sampling the objective is expensive (it requires a full rendering or simulator run), we devise an efficient sampling scheme that allows for tractable run-times and competitive performance at little overhead. We demonstrate optimizing diverse non-convex, non-differentiable black-box problems in graphics, such as visibility in rendering, discrete parameter spaces in procedural modelling or optimal control in physics-driven animation. In contrast to other derivative-free algorithms, our approach scales well to higher dimensions, which we demonstrate on problems with up to 35k interlinked variables. △ Less

Submitted 7 May, 2024; v1 submitted 10 August, 2023; originally announced August 2023.

Comments: Accepted at SIGGRAPH 2024. Project page: https://mfischer-ucl.github.io/zerograds

Mathematical meal models for simulation of human metabolism

Authors: Tobias K. S. Ritschel, Asbjørn Thode Reenberg, Peter Emil Carstensen, Jacob Bendsen, John Bagterp Jørgensen

Abstract: We present and critically discuss five commonly used mathematical models of the meal glucose rate of appearance in humans. Such models are key to simulation of the metabolism in healthy people, people with diabetes, and obese people, and they are central to developing effective treatments and prevention strategies. Furthermore, we discuss important aspects of systematic mathematical modeling of hu… ▽ More We present and critically discuss five commonly used mathematical models of the meal glucose rate of appearance in humans. Such models are key to simulation of the metabolism in healthy people, people with diabetes, and obese people, and they are central to developing effective treatments and prevention strategies. Furthermore, we discuss important aspects of systematic mathematical modeling of human metabolism, including meal consumption modeling, stoichiometry and reaction kinetics, and general-purpose model components. △ Less

Submitted 31 July, 2023; originally announced July 2023.

Comments: 17 pages, 9 figures, 2 tables

Quantitative modeling and simulation of biochemical processes in the human body

Authors: Jacob Bendsen, Peter Emil Carstensen, Asbjørn Thode Reenberg, Tobias K. S. Ritschel, John Bagterp Jørgensen

Abstract: We present a whole-body model of human metabolism that utilizes a system of organs and blood vessels to simulate the enzymatic reactions. The model focuses on key organs, including the brain, heart and lungs, liver, gut, and kidney, as well as muscle and adipose tissue. The model equations are formulated using stoichiometry and Michaelis-Menten kinetics to describe the enzymatic reactions. We demo… ▽ More We present a whole-body model of human metabolism that utilizes a system of organs and blood vessels to simulate the enzymatic reactions. The model focuses on key organs, including the brain, heart and lungs, liver, gut, and kidney, as well as muscle and adipose tissue. The model equations are formulated using stoichiometry and Michaelis-Menten kinetics to describe the enzymatic reactions. We demonstrate how the model can be used to simulate the effects of prolonged fasting and intermittent fasting on selected metabolite concentrations and glucose flux. Furthermore, by simulating intermittent fasting the effect on the carbohydrate, the protein and the lipid storage is examined. We propose this method as a simple and intuitive approach for modeling the human metabolism, which is general, systematic and easy to incorporate. This could have potential applications in PK/PD drug development and in understanding metabolic disorders. △ Less

Submitted 30 July, 2023; originally announced July 2023.

Comments: 18 pages, 12 figures, 7 tables. arXiv admin note: text overlap with arXiv:2205.01473

Dynamics of electronic states in the insulating Intermediate surface phase of 1T-TaS$_2$

Authors: Jingwei Dong, Weiyan Qi, Dongbin Shin, Laurent Cario, Zhesheng Chen, Romain Grasset, Davide Boschetto, Mateusz Weis, Pierrick Lample, Ernest Pastor, Tobias Ritschel, Marino Marsi, Amina Taleb, Noejung Park, Angel Rubio, Evangelos Papalazarou, Luca Perfetti

Abstract: This article reports a comparative study of bulk and surface properties in the transition metal dichalcogenide 1T-TaS$_2$. When heating the sample, the surface displays an intermediate insulating phase that persists for $\sim 10$ K on top of a metallic bulk. The weaker screening of Coulomb repulsion and stiffer Charge Density Wave (CDW) explain such resilience of a correlated insulator in the topm… ▽ More This article reports a comparative study of bulk and surface properties in the transition metal dichalcogenide 1T-TaS$_2$. When heating the sample, the surface displays an intermediate insulating phase that persists for $\sim 10$ K on top of a metallic bulk. The weaker screening of Coulomb repulsion and stiffer Charge Density Wave (CDW) explain such resilience of a correlated insulator in the topmost layers. Both time resolved ARPES and transient reflectivity are employed to investigate the dynamics of electrons and CDW collective motion. It follows that the amplitude mode is always stiffer at the surface and displays variable coupling to the Mott-Peierls band, stronger in the low temperature phase and weaker in the intermediate one. △ Less

Submitted 31 October, 2023; v1 submitted 12 July, 2023; originally announced July 2023.

Megahertz X-ray Multi-projection imaging

Authors: Pablo Villanueva-Perez, Valerio Bellucci, Yuhe Zhang, Sarlota Birnsteinova, Rita Graceffa, Luigi Adriano, Eleni Myrto Asimakopoulou, Ilia Petrov, Zisheng Yao, Marco Romagnoni, Andrea Mazzolari, Romain Letrun, Chan Kim, Jayanath C. P. Koliyadu, Carsten Deiter, Richard Bean, Gabriele Giovanetti, Luca Gelisio, Tobias Ritschel, Adrian Mancuso, Henry N. Chapman, Alke Meents, Tokushi Sato, Patrik Vagovic

Abstract: X-ray time-resolved tomography is one of the most popular X-ray techniques to probe dynamics in three dimensions (3D). Recent developments in time-resolved tomography opened the possibility of recording kilohertz-rate 3D movies. However, tomography requires rotating the sample with respect to the X-ray beam, which prevents characterization of faster structural dynamics. Here, we present megahertz… ▽ More X-ray time-resolved tomography is one of the most popular X-ray techniques to probe dynamics in three dimensions (3D). Recent developments in time-resolved tomography opened the possibility of recording kilohertz-rate 3D movies. However, tomography requires rotating the sample with respect to the X-ray beam, which prevents characterization of faster structural dynamics. Here, we present megahertz (MHz) X-ray multi-projection imaging (MHz-XMPI), a technique capable of recording volumetric information at MHz rates and micrometer resolution without scanning the sample. We achieved this by harnessing the unique megahertz pulse structure and intensity of the European X-ray Free-electron Laser with a combination of novel detection and reconstruction approaches that do not require sample rotations. Our approach enables generating multiple X-ray probes that simultaneously record several angular projections for each pulse in the megahertz pulse burst. We provide a proof-of-concept demonstration of the MHz-XMPI technique's capability to probe 4D (3D+time) information on stochastic phenomena and non-reproducible processes three orders of magnitude faster than state-of-the-art time-resolved X-ray tomography, by generating 3D movies of binary droplet collisions. We anticipate that MHz-XMPI will enable in-situ and operando studies that were impossible before, either due to the lack of temporal resolution or because the systems were opaque (such as for MHz imaging based on optical microscopy). △ Less

Submitted 19 May, 2023; originally announced May 2023.

Neural Field Convolutions by Repeated Differentiation

Authors: Ntumba Elie Nsampi, Adarsh Djeacoumar, Hans-Peter Seidel, Tobias Ritschel, Thomas Leimkühler

Abstract: Neural fields are evolving towards a general-purpose continuous representation for visual computing. Yet, despite their numerous appealing properties, they are hardly amenable to signal processing. As a remedy, we present a method to perform general continuous convolutions with general continuous signals such as neural fields. Observing that piecewise polynomial kernels reduce to a sparse set of D… ▽ More Neural fields are evolving towards a general-purpose continuous representation for visual computing. Yet, despite their numerous appealing properties, they are hardly amenable to signal processing. As a remedy, we present a method to perform general continuous convolutions with general continuous signals such as neural fields. Observing that piecewise polynomial kernels reduce to a sparse set of Dirac deltas after repeated differentiation, we leverage convolution identities and train a repeated integral field to efficiently execute large-scale convolutions. We demonstrate our approach on a variety of data modalities and spatially-varying kernels. △ Less

Submitted 4 April, 2024; v1 submitted 4 April, 2023; originally announced April 2023.

A Newton-like Method based on Model Reduction Techniques for Implicit Numerical Methods

Authors: Tobias K. S. Ritschel

Abstract: In this paper, we present a Newton-like method based on model reduction techniques, which can be used in implicit numerical methods for approximating the solution to ordinary differential equations. In each iteration, the Newton-like method solves a reduced order linear system in order to compute the Newton step. This reduced system is derived using a projection matrix, obtained using proper ortho… ▽ More In this paper, we present a Newton-like method based on model reduction techniques, which can be used in implicit numerical methods for approximating the solution to ordinary differential equations. In each iteration, the Newton-like method solves a reduced order linear system in order to compute the Newton step. This reduced system is derived using a projection matrix, obtained using proper orthogonal decomposition, which is updated in each time step of the numerical method. We demonstrate that the method can be used together with Euler's implicit method to simulate CO$_2$ injection into an oil reservoir, and we compare with using Newton's method. The Newton-like method achieves a speedup of between 39% and 84% for systems with between 4,800 and 52,800 state variables. △ Less

Submitted 13 March, 2023; originally announced March 2023.

Comments: 6 pages, 5 figures, 2 algorithms, Accepted for inclusion in the 2023 European Control Conference (ECC)

Software principles and concepts applied in the implementation of cyber-physical systems for real-time advanced process control

Authors: Anders H. D. Andersen, Zhanhao Zhang, Steen Hørsholt, Tobias K. S. Ritschel, John Bagterp Jørgensen

Abstract: Cyber-physical systems (CPSs) for real-time advanced process control (RT-APC) are a class of control systems using network communication to control industrial processes. In this paper, we use simple examples to describe the software principles and concepts used in the implementation of such systems. The key software principles are 1) shared data in the form of a database, files, or shared memory,… ▽ More Cyber-physical systems (CPSs) for real-time advanced process control (RT-APC) are a class of control systems using network communication to control industrial processes. In this paper, we use simple examples to describe the software principles and concepts used in the implementation of such systems. The key software principles are 1) shared data in the form of a database, files, or shared memory, 2) timers and threads for concurrent periodic execution of tasks, and 3) network communication between the control system and the process, and communication between the control system and the internet, e.g., the cloud to enable remote monitoring and commands. We show how to implement such systems for Linux operating systems applying the C programming language and we also comment on the implementation using the Python programming language. Finally, we present a complete simulation experiment using a real-time simulator. △ Less

Submitted 5 July, 2023; v1 submitted 27 February, 2023; originally announced February 2023.

Comments: 6 pages, 6 figures, 6 listings. Accepted for European Control Conference 2023 (ECC2023). Bucharest, Romania

Modeling, scientific computing and optimal control for renewable energy systems with storage

Authors: Nicola Cantisani, Tobias K. S. Ritschel, Christian A. Thilker, Henrik Madsen, John Bagterp Jørgensen

Abstract: This paper presents models for renewable energy systems with storage, and considers its optimal operation. We model and simulate wind and solar power production using stochastic differential equations as well as storage of the produced power using batteries, thermal storage, and water electrolysis. We formulate an economic optimal control problem, with the scope of controlling the system in the mo… ▽ More This paper presents models for renewable energy systems with storage, and considers its optimal operation. We model and simulate wind and solar power production using stochastic differential equations as well as storage of the produced power using batteries, thermal storage, and water electrolysis. We formulate an economic optimal control problem, with the scope of controlling the system in the most efficient way, while satisfying the power demand from the electric grid. Deploying multiple storage systems allows flexibility and higher reliability of the renewable energy system. △ Less

Submitted 17 December, 2022; originally announced December 2022.

Comments: Submitted to European Control Conference 2023

Model-based control algorithms for the quadruple tank system: An experimental comparison

Authors: Anders H. D. Andersen, Tobias K. S. Ritschel, Steen Hørsholt, Jakob Kjøbsted Huusom, John Bagterp Jørgensen

Abstract: We compare the performance of proportional-integral-derivative (PID) control, linear model predictive control (LMPC), and nonlinear model predictive control (NMPC) for a physical setup of the quadruple tank system (QTS). We estimate the parameters in a continuous-discrete time stochastic nonlinear model for the QTS using a prediction-error-method based on the measured process data and a maximum li… ▽ More We compare the performance of proportional-integral-derivative (PID) control, linear model predictive control (LMPC), and nonlinear model predictive control (NMPC) for a physical setup of the quadruple tank system (QTS). We estimate the parameters in a continuous-discrete time stochastic nonlinear model for the QTS using a prediction-error-method based on the measured process data and a maximum likelihood (ML) criterion. In the NMPC algorithm, we use this identified continuous-discrete time stochastic nonlinear model. The LMPC algorithm is based on a linearization of this nonlinear model. We tune the PID controller using Skogestad's IMC tuning rules using a transfer function representation of the linearized model. Norms of the observed tracking errors and the rate of change of the manipulated variables are used to compare the performance of the control algorithms. The LMPC and NMPC perform better than the PID controller for a predefined time-varying setpoint trajectory. The LMPC and NMPC algorithms have similar performance. △ Less

Submitted 9 December, 2022; originally announced December 2022.

Comments: 6 pages, 5 figures, 3 tables, to be published in Foundations of Computer Aided Process Operations / Chemical Process Control (FOCAPO/CPC 2023). Hilton San Antonio Hill Country, San Antonio, Texas

State Estimation for Continuous-Discrete-Time Nonlinear Stochastic Systems

Authors: Marcus Krogh Nielsen, Tobias K. S. Ritschel, Ib Christensen, Jess Dragheim, Jakob Kjøbsted Huusom, Krist V. Gernaey, John Bagterp Jørgensen

Abstract: State estimation incorporates the feedback in optimization based advanced process control systems and is very important for the performance of model predictive control. We describe the extended Kalman filter, the unscented Kalman filter, the ensemble Kalman filter, and a particle filter for continuous-discrete time nonlinear systems involving stochastic differential equations. Continuous-discrete… ▽ More State estimation incorporates the feedback in optimization based advanced process control systems and is very important for the performance of model predictive control. We describe the extended Kalman filter, the unscented Kalman filter, the ensemble Kalman filter, and a particle filter for continuous-discrete time nonlinear systems involving stochastic differential equations. Continuous-discrete time nonlinear systems is a natural way to model physical systems controlled by digital controllers. We implement the state estimation methods in Matlab, illustrate and evaluate their performance using simulations of the modified four-tank system. This system is non-stiff and the state estimation methods are implemented numerically using an explicit numerical integration scheme. We evaluate the accuracy of the state estimation methods in terms of the mean absolute percentage error over the simulation horizon. Each method successfully estimates the states and unmeasured disturbances of the simulated modified four-tank system. The key contribution is an overview and comparison of state estimation methods for continuous-discrete time nonlinear stochastic systems. This can guide efficient implementations. △ Less

Submitted 5 December, 2022; originally announced December 2022.

Comments: 6 pages, 1 figure, 1 table, to be published in proceedings of FOCAPO/CPC 2023. arXiv admin note: substantial text overlap with arXiv:2205.02730

Plateau-reduced Differentiable Path Tracing

Authors: Michael Fischer, Tobias Ritschel

Abstract: Current differentiable renderers provide light transport gradients with respect to arbitrary scene parameters. However, the mere existence of these gradients does not guarantee useful update steps in an optimization. Instead, inverse rendering might not converge due to inherent plateaus, i.e., regions of zero gradient, in the objective function. We propose to alleviate this by convolving the high-… ▽ More Current differentiable renderers provide light transport gradients with respect to arbitrary scene parameters. However, the mere existence of these gradients does not guarantee useful update steps in an optimization. Instead, inverse rendering might not converge due to inherent plateaus, i.e., regions of zero gradient, in the objective function. We propose to alleviate this by convolving the high-dimensional rendering function that maps scene parameters to images with an additional kernel that blurs the parameter space. We describe two Monte Carlo estimators to compute plateau-free gradients efficiently, i.e., with low variance, and show that these translate into net-gains in optimization error and runtime performance. Our approach is a straightforward extension to both black-box and differentiable renderers and enables optimization of problems with intricate light transport, such as caustics or global illumination, that existing differentiable renderers do not converge on. △ Less

Submitted 28 March, 2023; v1 submitted 30 November, 2022; originally announced November 2022.

Comments: Accepted to CVPR 2023. Project page and interactive demos at https://mfischer-ucl.github.io/prdpt/

3inGAN: Learning a 3D Generative Model from Images of a Self-similar Scene

Authors: Animesh Karnewar, Oliver Wang, Tobias Ritschel, Niloy Mitra

Abstract: We introduce 3inGAN, an unconditional 3D generative model trained from 2D images of a single self-similar 3D scene. Such a model can be used to produce 3D "remixes" of a given scene, by mapping spatial latent codes into a 3D volumetric representation, which can subsequently be rendered from arbitrary views using physically based volume rendering. By construction, the generated scenes remain view-c… ▽ More We introduce 3inGAN, an unconditional 3D generative model trained from 2D images of a single self-similar 3D scene. Such a model can be used to produce 3D "remixes" of a given scene, by mapping spatial latent codes into a 3D volumetric representation, which can subsequently be rendered from arbitrary views using physically based volume rendering. By construction, the generated scenes remain view-consistent across arbitrary camera configurations, without any flickering or spatio-temporal artifacts. During training, we employ a combination of 2D, obtained through differentiable volume tracing, and 3D Generative Adversarial Network (GAN) losses, across multiple scales, enforcing realism on both its 3D structure and the 2D renderings. We show results on semi-stochastic scenes of varying scale and complexity, obtained from real and synthetic sources. We demonstrate, for the first time, the feasibility of learning plausible view-consistent 3D scene variations from a single exemplar scene and provide qualitative and quantitative comparisons against recent related methods. △ Less

Submitted 27 November, 2022; originally announced November 2022.

Comments: Conference accept at 3DV 2022

Estimating a Personalized Basal Insulin Dose from Short-Term Closed-Loop Data in Type 2 Diabetes

Authors: Sarah Ellinor Engell, Tinna Björk Aradóttir, Tobias K. S. Ritschel, Henrik Bengtsson, John Bagterp Jørgensen

Abstract: In type 2 diabetes (T2D) treatment, finding a safe and effective basal insulin dose is a challenge. The dose-response is highly individual and to ensure safety, people with T2D titrate by slowly increasing the daily insulin dose to meet treatment targets. This titration can take months. To ease and accelerate the process, we use short-term artificial pancreas (AP) treatment tailored for initial ti… ▽ More In type 2 diabetes (T2D) treatment, finding a safe and effective basal insulin dose is a challenge. The dose-response is highly individual and to ensure safety, people with T2D titrate by slowly increasing the daily insulin dose to meet treatment targets. This titration can take months. To ease and accelerate the process, we use short-term artificial pancreas (AP) treatment tailored for initial titration and apply it as a diagnostic tool. Specifically, we present a method to automatically estimate a personalized daily dose of basal insulin from closed-loop data collected with an AP. Based on AP-data from a stochastic simulation model, we employ the continuous-discrete extended Kalman filter and a maximum likelihood approach to estimate parameters in a simple dose-response model for 100 virtual people. With the identified model, we compute a daily dose of basal insulin to meet treatment targets for each individual. We test the personalized dose and evaluate the treatment outcomes against clinical reference values. In the tested simulation setup, the proposed method is feasible. However, more extensive tests will reveal whether it can be deemed safe for clinical implementation. △ Less

Submitted 25 November, 2022; originally announced November 2022.

Comments: 6 pages, 4 figures, 1 table. Accepted for publication in Proceedings of the 2022 61st IEEE Conference on Decision and Control (CDC)

Learning to Rasterize Differentiably

Authors: Chenghao Wu, Hamila Mailee, Zahra Montazeri, Tobias Ritschel

Abstract: Differentiable rasterization changes the standard formulation of primitive rasterization -- by enabling gradient flow from a pixel to its underlying triangles -- using distribution functions in different stages of rendering, creating a "soft" version of the original rasterizer. However, choosing the optimal softening function that ensures the best performance and convergence to a desired goal requ… ▽ More Differentiable rasterization changes the standard formulation of primitive rasterization -- by enabling gradient flow from a pixel to its underlying triangles -- using distribution functions in different stages of rendering, creating a "soft" version of the original rasterizer. However, choosing the optimal softening function that ensures the best performance and convergence to a desired goal requires trial and error. Previous work has analyzed and compared several combinations of softening. In this work, we take it a step further and, instead of making a combinatorial choice of softening operations, parameterize the continuous space of common softening operations. We study meta-learning tunable softness functions over a set of inverse rendering tasks (2D and 3D shape, pose and occlusion) so it generalizes to new and unseen differentiable rendering tasks with optimal softness. △ Less

Submitted 15 July, 2024; v1 submitted 23 November, 2022; originally announced November 2022.

Comments: Published at Computer Graphics Forum (EGSR 2024), code see https://github.com/Theo-Wu/MetaRas, project webpage see https://theo-wu.github.io/MetaRas/

Electronic dispersion, correlations and stacking in the photoexcited state of 1T-TaS$_2$

Authors: Jingwei Dong, Dongbin Shin, Ernest Pastor, Tobias Ritschel, Laurent Cario, Zhesheng Chen, Weyain Qi, Romain Grasset, Marino Marsi, Amina Taleb-Ibrahimi, Noejung Park, Angel Rubio, Luca Perfetti, Evangelos Papalazarou

Abstract: Here we perform angle and time-resolved photoelectron spectroscopy on the commensurate Charge Density Wave (CDW) phase of 1T-TaS$_2$. Data with different probe pulse polarization are employed to map the dispersion of electronic states below and above the chemical potential. Upon photoexcitation, the fluctuations of CDW order erase the band dispersion near to the chemical potential and halve the ch… ▽ More Here we perform angle and time-resolved photoelectron spectroscopy on the commensurate Charge Density Wave (CDW) phase of 1T-TaS$_2$. Data with different probe pulse polarization are employed to map the dispersion of electronic states below and above the chemical potential. Upon photoexcitation, the fluctuations of CDW order erase the band dispersion near to the chemical potential and halve the charge gap size. This transient phase sets within half a period of the coherent lattice motion and is favored by strong electronic correlations. The experimental results are compared to Density-Functional Theory (DFT) calculations with a self-consistent evaluation of the Coulomb repulsion. Our simulations indicate that the screening of Coulomb repulsion depends on the stacking order of the TaS$_2$ layers. The entanglement of such degrees of freedom suggest that both the structural order and electronic repulsion are locally modified by the photoinduced CDW fluctuations. △ Less

Submitted 14 June, 2023; v1 submitted 20 October, 2022; originally announced October 2022.

Learning to Learn and Sample BRDFs

Authors: Chen Liu, Michael Fischer, Tobias Ritschel

Abstract: We propose a method to accelerate the joint process of physically acquiring and learning neural Bi-directional Reflectance Distribution Function (BRDF) models. While BRDF learning alone can be accelerated by meta-learning, acquisition remains slow as it relies on a mechanical process. We show that meta-learning can be extended to optimize the physical sampling pattern, too. After our method has be… ▽ More We propose a method to accelerate the joint process of physically acquiring and learning neural Bi-directional Reflectance Distribution Function (BRDF) models. While BRDF learning alone can be accelerated by meta-learning, acquisition remains slow as it relies on a mechanical process. We show that meta-learning can be extended to optimize the physical sampling pattern, too. After our method has been meta-trained for a set of fully-sampled BRDFs, it is able to quickly train on new BRDFs with up to five orders of magnitude fewer physical acquisition samples at similar quality. Our approach also extends to other linear and non-linear BRDF models, which we show in an extensive evaluation. △ Less

Submitted 4 May, 2023; v1 submitted 7 October, 2022; originally announced October 2022.

Comments: Accepted to Eurographics 2023; Project Page at https://ryushinn.github.io/metasampling

Pressure-tuning of $α$-RuCl$_3$ towards the ideal Kitaev-limit

Authors: Quirin Stahl, Tobias Ritschel, Gaston Garbarino, Frederico Cova, Anna Isaeva, Thomas Doert, Jochen Geck

Abstract: We report the discovery of an intriguing pressure-driven phase transformation in the layered Kitaev-material $α$-RuCl$_3$. By analyzing both the Bragg scattering as well as the diffuse scattering of high-quality single crystals, we reveal a collective reorganization of the layer stacking throughout the crystal. Importantly, this transformation also effects the structure of the RuCl$_3$ honeycomb l… ▽ More We report the discovery of an intriguing pressure-driven phase transformation in the layered Kitaev-material $α$-RuCl$_3$. By analyzing both the Bragg scattering as well as the diffuse scattering of high-quality single crystals, we reveal a collective reorganization of the layer stacking throughout the crystal. Importantly, this transformation also effects the structure of the RuCl$_3$ honeycomb layers, which acquire a high trigonal symmetry with a single Ru--Ru distance of 3.41Å and a single Ru--Cl--Ru bond angle of 92.8°. Hydrostatic pressure therefore allows to tune the structure of $α$-RuCl$_3$ much closer to the ideal Kitaev-limit. The high-symmetry phase can also be stabilized by biaxial stress, which can explain conflicting results reported earlier and, more importantly, makes the high-symmetry phase accessible to a variety of experiments. △ Less

Submitted 17 September, 2022; originally announced September 2022.

ReLU Fields: The Little Non-linearity That Could

Authors: Animesh Karnewar, Tobias Ritschel, Oliver Wang, Niloy J. Mitra

Abstract: In many recent works, multi-layer perceptions (MLPs) have been shown to be suitable for modeling complex spatially-varying functions including images and 3D scenes. Although the MLPs are able to represent complex scenes with unprecedented quality and memory footprint, this expressive power of the MLPs, however, comes at the cost of long training and inference times. On the other hand, bilinear/tri… ▽ More In many recent works, multi-layer perceptions (MLPs) have been shown to be suitable for modeling complex spatially-varying functions including images and 3D scenes. Although the MLPs are able to represent complex scenes with unprecedented quality and memory footprint, this expressive power of the MLPs, however, comes at the cost of long training and inference times. On the other hand, bilinear/trilinear interpolation on regular grid based representations can give fast training and inference times, but cannot match the quality of MLPs without requiring significant additional memory. Hence, in this work, we investigate what is the smallest change to grid-based representations that allows for retaining the high fidelity result of MLPs while enabling fast reconstruction and rendering times. We introduce a surprisingly simple change that achieves this task -- simply allowing a fixed non-linearity (ReLU) on interpolated grid values. When combined with coarse to-fine optimization, we show that such an approach becomes competitive with the state-of-the-art. We report results on radiance fields, and occupancy fields, and compare against multiple existing alternatives. Code and data for the paper are available at https://geometry.cs.ucl.ac.uk/projects/2022/relu_fields. △ Less

Submitted 2 July, 2023; v1 submitted 22 May, 2022; originally announced May 2022.

Comments: Published at SIGGRAPH 2022

State Estimation Methods for Continuous-Discrete Nonlinear Systems involving Stochastic Differential Equations

Authors: Marcus Krogh Nielsen, Tobias K. S. Ritschel, Ib Christensen, Jess Dragheim, Jakob Kjøbsted Huusom, Krist V. Gernaey, John Bagterp Jørgensen

Abstract: In this work, we present methods for state estimation in continuous-discrete nonlinear systems involving stochastic differential equations. We present the extended Kalman filter, the unscented Kalman filter, the ensemble Kalman filter, and a particle filter. We implement the state estimation methods in Matlab. We evaluate the performance of the methods on a simulation of the modified four-tank sys… ▽ More In this work, we present methods for state estimation in continuous-discrete nonlinear systems involving stochastic differential equations. We present the extended Kalman filter, the unscented Kalman filter, the ensemble Kalman filter, and a particle filter. We implement the state estimation methods in Matlab. We evaluate the performance of the methods on a simulation of the modified four-tank system. We implement the state estimation methods for non-stiff systems, i.e., using an explicit numerical integration scheme. The implementation of the extended Kalman filter utilises the Joseph stabilising form for numerical stability. We evaluate the accuracy of the state estimation methods in terms of the mean absolute percentage error over the simulation horizon. We show that each method successfully estimates the states and unmeasured disturbances of the simulated modified four-tank system. Finally, we present conclusions. △ Less

Submitted 5 May, 2022; originally announced May 2022.

Comments: 6 pages, 2 figures, 1 table, submitted to be presented at a conference

A whole-body multi-scale mathematical model for dynamic simulation of the metabolism in man

Authors: Peter Emil Carstensen, Jacob Bendsen, Asbjørn Thode Reenberg, Tobias K. S. Ritschel, John Bagterp Jørgensen

Abstract: We propose a whole-body model of the metabolism in man as well as a generalized approach for modeling metabolic networks. Using this approach, we are able to write a large metabolic network in a systematic and compact way. We demonstrate the approach using a whole-body model of the metabolism of the three macronutrients, carbohydrates, proteins and lipids. The model contains 7 organs, 16 metabolit… ▽ More We propose a whole-body model of the metabolism in man as well as a generalized approach for modeling metabolic networks. Using this approach, we are able to write a large metabolic network in a systematic and compact way. We demonstrate the approach using a whole-body model of the metabolism of the three macronutrients, carbohydrates, proteins and lipids. The model contains 7 organs, 16 metabolites and 31 enzymatic reactions. All reaction rates are described by Michaelis-Menten kinetics with an addition of a hormonal regulator based on the two hormones insulin and glucagon. We incorporate ingestion of food in order to simulate metabolite concentrations during the feed-fast cycle. The model can simulate several days due to the inclusion of storage forms (glycogen, muscle protein and lipid droplets), that can be depleted if food is not ingested regularly. A physiological model incorporating complex cellular metabolism and whole-body mass dynamics can be used in virtual clinical trials. Such trials can be used to improve the development of medicine, treatment strategies such as control algorithms, and increase the likelihood of a successful clinical trial. △ Less

Submitted 3 May, 2022; originally announced May 2022.

Comments: 6 pages, 7 figures, submitted to be presented at a conference

Large-scale Virtual Clinical Trials of Closed-loop Treatments for People with Type 1 Diabetes

Authors: Tobias K. S. Ritschel, Asbjørn Thode Reenberg, John Bagterp Jørgensen

Abstract: We propose a virtual clinical trial for assessing the safety and efficacy of closed-loop diabetes treatments prior to an actual clinical trial. Such virtual trials enable rapid and risk-free pretrial testing of algorithms, and they can be used to compare different treatment variations for large and diverse populations. The participants are represented by multiple mathematical models, consisting of… ▽ More We propose a virtual clinical trial for assessing the safety and efficacy of closed-loop diabetes treatments prior to an actual clinical trial. Such virtual trials enable rapid and risk-free pretrial testing of algorithms, and they can be used to compare different treatment variations for large and diverse populations. The participants are represented by multiple mathematical models, consisting of stochastic differential equations, and we use Monte Carlo closed-loop simulations to compute detailed statistics of the closed-loop treatments. We implement the virtual clinical trial using high-performance software and hardware, and we present an example trial with two mathematical models of one~million participants over 52~weeks (i.e., two~million simulations), which can be completed in 2~h 9~min. △ Less

Submitted 3 May, 2022; originally announced May 2022.

Comments: 6 pages, 2 tables, 5 figures, in submission for presentation at a conference