7 papers
RealPDEBench: A Benchmark for Complex Physical Systems with Real-World Data
Peiyan Hu, Haodong Feng, Hongyuan Liu +13
Predicting the evolution of complex physical systems remains a central problem in science and engineering. Despite rapid progress in scientific Machine Learning (ML) models, a crit…
GenCP: Towards Generative Modeling Paradigm of Coupled Physics
Tianrun Gao, Haoren Zheng, Wenhao Deng +5
Real-world physical systems are inherently complex, often involving the coupling of multiple physics, making their simulation both highly valuable and challenging. Many mainstream…
Towards an AI Fluid Scientist: LLM-Powered Scientific Discovery in Experimental Fluid Mechanics
Haodong Feng, Lugang Ye, Dixia Fan
The integration of artificial intelligence into experimental fluid mechanics promises to accelerate discovery, yet most AI applications remain narrowly focused on numerical studies…
Model-Based Closed-Loop Control Algorithm for Stochastic Partial Differential Equation Control
Peiyan Hu, Haodong Feng, Yue Wang +1
Neural operators have demonstrated promise in modeling and controlling systems governed by Partial Differential Equations (PDEs). Beyond PDEs, Stochastic Partial Differential Equat…
How to Re-enable PDE Loss for Physical Systems Modeling Under Partial Observation
Haodong Feng, Yue Wang, Dixia Fan
In science and engineering, machine learning techniques are increasingly successful in physical systems modeling (predicting future states of physical systems). Effectively integra…
Wavelet Diffusion Neural Operator
Peiyan Hu, Rui Wang, Xiang Zheng +7
Simulating and controlling physical systems described by partial differential equations (PDEs) are crucial tasks across science and engineering. Recently, diffusion generative mode…