1 citations · 1 across the 10 of their papers we have counts for
10 papers
Physical-Field Reconstruction from Sparse Observations: When Are Diffusion Models Preferable to Deterministic Regression?
Hao Zhou, Rui Zhang, Qi Wang +1
Reconstructing physical fields from sparse observations is central to system identification, forecasting, and control, yet sparse measurements generally underdetermine the full fie…
Large language models for partial differential equation workflows
Han Wan, Rui Zhang, Hao Sun
Numerical methods and deep learning have advanced the modelling and simulation of systems governed by partial differential equations (PDEs), while formulating equations, configurin…
Learning, Solving and Optimizing PDEs with TensorGalerkin: an efficient high-performance Galerkin assembly algorithm
Shizheng Wen, Mingyuan Chi, Tianwei Yu +5
We present a unified algorithmic framework for the numerical solution, constrained optimization, and physics-informed learning of PDEs with a variational structure. Our framework i…
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…
Hierarchical Physics-Embedded Learning for Partially Known Spatiotemporal Dynamics
Xizhe Wang, Xiaobin Song, Hongbo Zhao +4
Partial physical knowledge--governing structures known, constitutive relations or their combinations not--pervades spatiotemporal systems. Existing scientific machine learning para…
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…
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…