Showing 1–6 of 6 results for author: Zhan, D

FLUME-FNO: data-efficient and scalable prediction of 3D wind and temperature fields in unseen urban morphologies

Authors: Shaoxiang Qin, Theodore Potsis, Dongxue Zhan, Xue Liu, Ted Stahopoulos, Liangzhu Leon Wang

Abstract: Urban microclimate, encompassing wind and temperature fields shaped by building geometry, significantly impacts energy consumption, pedestrian winds, pollutant dispersion, urban heat island, and public health. Accurately predicting microclimate is crucial yet challenging. Conventional Computational Fluid Dynamics (CFD) is computationally prohibitive for rapid assessments, while many deep learning… ▽ More Urban microclimate, encompassing wind and temperature fields shaped by building geometry, significantly impacts energy consumption, pedestrian winds, pollutant dispersion, urban heat island, and public health. Accurately predicting microclimate is crucial yet challenging. Conventional Computational Fluid Dynamics (CFD) is computationally prohibitive for rapid assessments, while many deep learning approaches require extensive training data and struggle with generalization in unseen configurations. We present the Fast Localized Urban Microclimate Emulation Fourier Neural Operator (FLUME-FNO), a data-efficient and scalable framework for rapid prediction of 3D wind and temperature fields based solely on building geometry. FLUME-FNO assumes the local urban microclimate is primarily governed by surrounding geometry directly visible from a specific location. To encode this, the framework introduces a novel Multi-Directional Distance Feature (MDDF), representing visible open-space structures by measuring directional distances to surrounding buildings. By computing MDDF over the full domain and cropping encoded geometric features into smaller 3D patches, FLUME-FNO effectively augments limited CFD data, enabling robust learning from just 23 CFD simulations. The model achieves mean absolute errors of 0.2 m/s for wind speed and 0.19 °C for temperature on unseen configurations. Addressing the need for trustworthy fast microclimate prediction, the framework is further assessed using a deep ensemble as a practical proxy for FLUME-FNO uncertainty, ranging from 3% to 40% depending on location. The UQ framework demonstrates FLUME-FNO provides resilient, trustworthy predictions within acceptable accuracy thresholds for wind engineering and microclimate studies, highlighting its potential for real-world applications. △ Less

Submitted 19 May, 2026; v1 submitted 25 March, 2025; originally announced March 2025.

Generalization of Urban Wind Environment Using Fourier Neural Operator Across Different Wind Directions and Cities

Authors: Cheng Chen, Geng Tian, Shaoxiang Qin, Senwen Yang, Dingyang Geng, Dongxue Zhan, Jinqiu Yang, David Vidal, Liangzhu Leon Wang

Abstract: Simulation of urban wind environments is crucial for urban planning, pollution control, and renewable energy utilization. However, the computational requirements of high-fidelity computational fluid dynamics (CFD) methods make them impractical for real cities. To address these limitations, this study investigates the effectiveness of the Fourier Neural Operator (FNO) model in predicting flow field… ▽ More Simulation of urban wind environments is crucial for urban planning, pollution control, and renewable energy utilization. However, the computational requirements of high-fidelity computational fluid dynamics (CFD) methods make them impractical for real cities. To address these limitations, this study investigates the effectiveness of the Fourier Neural Operator (FNO) model in predicting flow fields under different wind directions and urban layouts. In this study, we investigate the effectiveness of the Fourier Neural Operator (FNO) model in predicting urban wind conditions under different wind directions and urban layouts. By training the model on velocity data from large eddy simulation data, we evaluate the performance of the model under different urban configurations and wind conditions. The results show that the FNO model can provide accurate predictions while significantly reducing the computational time by 99%. Our innovative approach of dividing the wind field into smaller spatial blocks for training improves the ability of the FNO model to capture wind frequency features effectively. The SDF data also provides important spatial building information, enhancing the model's ability to recognize physical boundaries and generate more realistic predictions. The proposed FNO approach enhances the AI model's generalizability for different wind directions and urban layouts. △ Less

Submitted 9 January, 2025; originally announced January 2025.

Modeling Multivariable High-resolution 3D Urban Microclimate Using Localized Fourier Neural Operator

Authors: Shaoxiang Qin, Dongxue Zhan, Dingyang Geng, Wenhui Peng, Geng Tian, Yurong Shi, Naiping Gao, Xue Liu, Liangzhu Leon Wang

Abstract: Accurate urban microclimate analysis with wind velocity and temperature is vital for energy-efficient urban planning, supporting carbon reduction, enhancing public health and comfort, and advancing the low-altitude economy. However, traditional computational fluid dynamics (CFD) simulations that couple velocity and temperature are computationally expensive. Recent machine learning advancements off… ▽ More Accurate urban microclimate analysis with wind velocity and temperature is vital for energy-efficient urban planning, supporting carbon reduction, enhancing public health and comfort, and advancing the low-altitude economy. However, traditional computational fluid dynamics (CFD) simulations that couple velocity and temperature are computationally expensive. Recent machine learning advancements offer promising alternatives for accelerating urban microclimate simulations. The Fourier neural operator (FNO) has shown efficiency and accuracy in predicting single-variable velocity magnitudes in urban wind fields. Yet, for multivariable high-resolution 3D urban microclimate prediction, FNO faces three key limitations: blurry output quality, high GPU memory demand, and substantial data requirements. To address these issues, we propose a novel localized Fourier neural operator (Local-FNO) model that employs local training, geometry encoding, and patch overlapping. Local-FNO provides accurate predictions for rapidly changing turbulence in urban microclimate over 60 seconds, four times the average turbulence integral time scale, with an average error of 0.35 m/s in velocity and 0.30 °C in temperature. It also accurately captures turbulent heat flux represented by the velocity-temperature correlation. In a 2 km by 2 km domain, Local-FNO resolves turbulence patterns down to a 10 m resolution. It provides high-resolution predictions with 150 million feature dimensions on a single 32 GB GPU at nearly 50 times the speed of a CFD solver. Compared to FNO, Local-FNO achieves a 23.9% reduction in prediction error and a 47.3% improvement in turbulent fluctuation correlation. △ Less

Submitted 18 November, 2024; originally announced November 2024.

Physics-informed Inverse Design of Multi-bit Programmable Metasurfaces

Authors: Yucheng Xu, Jia-Qi Yang, Kebin Fan, Sheng Wang, Jingbo Wu, Caihong Zhang, De-Chuan Zhan, Willie J. Padilla, Biaobing Jin, Jian Chen, Peiheng Wu

Abstract: Emerging reconfigurable metasurfaces offer various possibilities in programmatically manipulating electromagnetic waves across spatial, spectral, and temporal domains, showcasing great potential for enhancing terahertz applications. However, they are hindered by limited tunability, particularly evident in relatively small phase tuning over 270o, due to the design constraints with time-intensive fo… ▽ More Emerging reconfigurable metasurfaces offer various possibilities in programmatically manipulating electromagnetic waves across spatial, spectral, and temporal domains, showcasing great potential for enhancing terahertz applications. However, they are hindered by limited tunability, particularly evident in relatively small phase tuning over 270o, due to the design constraints with time-intensive forward design methodologies. Here, we demonstrate a multi-bit programmable metasurface capable of terahertz beam steering, facilitated by a developed physics-informed inverse design (PIID) approach. Through integrating a modified coupled mode theory (MCMT) into residual neural networks, our PIID algorithm not only significantly increases the design accuracy compared to conventional neural networks but also elucidates the intricate physical relations between the geometry and the modes. Without decreasing the reflection intensity, our method achieves the enhanced phase tuning as large as 300o. Additionally, we experimentally validate the inverse designed programmable beam steering metasurface, which is adaptable across 1-bit, 2-bit, and tri-state coding schemes, yielding a deflection angle up to 68o and broadened steering coverage. Our demonstration provides a promising pathway for rapidly exploring advanced metasurface devices, with potentially great impact on communication and imaging technologies. △ Less

Submitted 26 May, 2024; originally announced May 2024.

Learning Robust Precipitation Forecaster by Temporal Frame Interpolation

Authors: Lu Han, Xu-Yang Chen, Han-Jia Ye, De-Chuan Zhan

Abstract: Recent advances in deep learning have significantly elevated weather prediction models. However, these models often falter in real-world scenarios due to their sensitivity to spatial-temporal shifts. This issue is particularly acute in weather forecasting, where models are prone to overfit to local and temporal variations, especially when tasked with fine-grained predictions. In this paper, we add… ▽ More Recent advances in deep learning have significantly elevated weather prediction models. However, these models often falter in real-world scenarios due to their sensitivity to spatial-temporal shifts. This issue is particularly acute in weather forecasting, where models are prone to overfit to local and temporal variations, especially when tasked with fine-grained predictions. In this paper, we address these challenges by developing a robust precipitation forecasting model that demonstrates resilience against such spatial-temporal discrepancies. We introduce Temporal Frame Interpolation (TFI), a novel technique that enhances the training dataset by generating synthetic samples through interpolating adjacent frames from satellite imagery and ground radar data, thus improving the model's robustness against frame noise. Moreover, we incorporate a unique Multi-Level Dice (ML-Dice) loss function, leveraging the ordinal nature of rainfall intensities to improve the model's performance. Our approach has led to significant improvements in forecasting precision, culminating in our model securing \textit{1st place} in the transfer learning leaderboard of the \textit{Weather4cast'23} competition. This achievement not only underscores the effectiveness of our methodologies but also establishes a new standard for deep learning applications in weather forecasting. Our code and weights have been public on \url{https://github.com/Secilia-Cxy/UNetTFI}. △ Less

Submitted 1 December, 2023; v1 submitted 30 November, 2023; originally announced November 2023.

Comments: Previous version has text overlap with last year's paper arXiv:2212.02968 since the competition's datasets does not change. We restate the dataset description to avoid it. We also polish the overall writing

IDToolkit: A Toolkit for Benchmarking and Developing Inverse Design Algorithms in Nanophotonics

Authors: Jia-Qi Yang, Yucheng Xu, Jia-Lei Shen, Kebin Fan, De-Chuan Zhan, Yang Yang

Abstract: Aiding humans with scientific designs is one of the most exciting of artificial intelligence (AI) and machine learning (ML), due to their potential for the discovery of new drugs, design of new materials and chemical compounds, etc. However, scientific design typically requires complex domain knowledge that is not familiar to AI researchers. Further, scientific studies involve professional skills… ▽ More Aiding humans with scientific designs is one of the most exciting of artificial intelligence (AI) and machine learning (ML), due to their potential for the discovery of new drugs, design of new materials and chemical compounds, etc. However, scientific design typically requires complex domain knowledge that is not familiar to AI researchers. Further, scientific studies involve professional skills to perform experiments and evaluations. These obstacles prevent AI researchers from developing specialized methods for scientific designs. To take a step towards easy-to-understand and reproducible research of scientific design, we propose a benchmark for the inverse design of nanophotonic devices, which can be verified computationally and accurately. Specifically, we implemented three different nanophotonic design problems, namely a radiative cooler, a selective emitter for thermophotovoltaics, and structural color filters, all of which are different in design parameter spaces, complexity, and design targets. The benchmark environments are implemented with an open-source simulator. We further implemented 10 different inverse design algorithms and compared them in a reproducible and fair framework. The results revealed the strengths and weaknesses of existing methods, which shed light on several future directions for developing more efficient inverse design algorithms. Our benchmark can also serve as the starting point for more challenging scientific design problems. The code of IDToolkit is available at https://github.com/ThyrixYang/IDToolkit. △ Less

Submitted 31 May, 2023; v1 submitted 30 May, 2023; originally announced May 2023.

Comments: KDD'23