6 papers
From Uncertain to Safe: Conformal Adaptation of Diffusion Models for Safe PDE Control
Peiyan Hu, Xiaowei Qian, Wenhao Deng +8
The application of deep learning for partial differential equation (PDE)-constrained control is gaining increasing attention. However, existing methods rarely consider safety requi…
Multi-modal Policies with Physics-informed Representations in Complex Fluid Environments
Haodong Feng, Peiyan Hu, Yue Wang +1
Control in fluid environments is an important research area with numerous applications across various domains, including underwater robotics, aerospace engineering, and biomedical…
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…
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…
DiffPhyCon: A Generative Approach to Control Complex Physical Systems
Long Wei, Peiyan Hu, Ruiqi Feng +7
Controlling the evolution of complex physical systems is a fundamental task across science and engineering. Classical techniques suffer from limited applicability or huge computati…