11 citations · 26 across the 16 of their papers we have counts for
6 papers · 1 filter
Stable spectral neural operator for learning stiff PDE systems from limited data
Rui Zhang, Han Wan, Yang Liu +1
Accurate modeling of spatiotemporal dynamics is crucial to understanding complex phenomena across science and engineering. However, this task faces a fundamental challenge when the…
Benchmarking neural surrogates on realistic spatiotemporal multiphysics flows
Runze Mao, Rui Zhang, Xuan Bai +21
Predicting multiphysics dynamics is computationally expensive and challenging due to the severe coupling of multi-scale, heterogeneous physical processes. While neural surrogates p…
Flexible Concept Bottleneck Model
Xingbo Du, Qiantong Dou, Lei Fan +1
Concept bottleneck models (CBMs) improve neural network interpretability by introducing an intermediate layer that maps human-understandable concepts to predictions. Recent work ha…
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…
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…
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…