5 papers · 1 filter
Spectral-inspired Operator Learning with Limited Data and Unknown Physics
Han Wan, Rui Zhang, Hao Sun
Learning PDE dynamics from limited data with unknown physics is challenging. Existing neural PDE solvers either require large datasets or rely on known physics (e.g., PDE residuals…
PerFlow: Physics-Embedded Rectified Flow for Efficient Reconstruction and Uncertainty Quantification of Spatiotemporal Dynamics
Hao Zhou, Rui Zhang, Han Wan +1
Reconstructing PDE-governed fields from sparse and irregular measurements is challenging due to their ill-posed nature. Deterministic surrogates are trained on dense fields that st…
Geometry-Aware Neural Optimizer for Shape Optimization and Inversion
Guoze Sun, Tianya Miao, Haoyang Huang +4
Geometry is central to PDE-governed systems, motivating shape optimization and inversion. Classical pipelines conduct costly forward simulation with geometry processing, requiring…
PIMRL: Physics-Informed Multi-Scale Recurrent Learning for Burst-Sampled Spatiotemporal Dynamics
Han Wan, Qi Wang, Yuan Mi +2
Deep learning has shown strong potential in modeling complex spatiotemporal dynamics. However, most existing methods depend on densely and uniformly sampled data, which is often un…
PeSANet: Physics-encoded Spectral Attention Network for Simulating PDE-Governed Complex Systems
Han Wan, Rui Zhang, Qi Wang +2
Accurately modeling and forecasting complex systems governed by partial differential equations (PDEs) is crucial in various scientific and engineering domains. However, traditional…