Portrait de Shaoxiang Qin n'est pas disponible

Shaoxiang Qin

Doctorat - McGill
Superviseur⋅e principal⋅e
Xue (Steve) Liu
Sujets de recherche
Apprentissage profond
Apprentissage spectral

Publications

From Large-Scale Winds to Urban Decision Making: A Cross-Scale Framework for Wind-Aware UAV Navigation
Shaoxiang Qin
Fuyuan Lyu
Di Zhou
Xue Liu
Xiongye Xiao
Anima Anandkumar
Liangzhu Leon Wang
Large-scale weather and climate models provide reliable wind information at regional scales, yet their outputs are typically too coarse for … (voir plus)direct UAV decision making in geometrically complex urban environments. This paper investigates how large-scale atmospheric information can be transformed into city-scale wind representations and utilized for downstream navigation decisions. We propose a cross-scale prediction and decision framework that takes background wind conditions from existing weather or climate models and combines them with detailed 3D urban geometry to predict time-averaged urban wind fields using a 3D neural operator. The predicted wind fields are then incorporated into a wind-aware UAV trajectory optimization problem to minimize energy consumption under kinematic feasibility and safety constraints. By comparing trajectories planned against a wind-agnostic baseline, we demonstrate significant efficiency gains enabled by AI-predicted wind, specifically 10.3% savings in tailwinds, 7.7% in headwinds, and 3.9% in crosswind conditions. These results indicate that learning decision-relevant urban wind representations offers a practical pathway for bridging large-scale atmospheric information and fine-scale urban decision making.
2026-02-28
AI_and_PDE @ International Conference on Learning Representations (poster)
openreview.net
openreview.net
Modeling Multivariable High-resolution 3D Urban Microclimate Using Localized Fourier Neural Operator
Shaoxiang Qin
Dongxue Zhan
Dingyang Geng
Wenhui Peng
Geng Tian
Yurong Shi
Naiping Gao
Xue Liu
Liangzhu (Leon) Wang
Accurate urban microclimate analysis with wind velocity and temperature is vital for energy-efficient urban planning, supporting carbon redu… (voir plus)ction, 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.
2025-02-11
Building and Environment (publié)
doi.org
arxiv.org