5 papers
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…
Differentiable Sparse Identification of Lagrangian Dynamics
Zitong Zhang, Hao Sun
Data-driven discovery of governing equations from data remains a fundamental challenge in nonlinear dynamics. Although sparse regression techniques have advanced system identificat…
OmniFluids: Physics Pre-trained Modeling of Fluid Dynamics
Rui Zhang, Qi Meng, Han Wan +3
Computational fluid dynamics (CFD) drives progress in numerous scientific and engineering fields, yet high-fidelity simulations remain computationally prohibitive. While machine le…
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…
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…