1 citations · 1 across the 4 of their papers we have counts for
4 papers
Do Physics Foundation Models Learn Generalizable Physics? A Bias-Aware Benchmark Across Physical Regimes and Distribution Shifts
Mengdi Chu, Yang Liu, Ayan Biswas +1
Recent physics foundation models claim general spatiotemporal forecasting ability, yet their evaluations often collapse performance into a single average score under a fixed traini…
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…
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…
PINP: Physics-Informed Neural Predictor with latent estimation of fluid flows
Huaguan Chen, Yang Liu, Hao Sun
Accurately predicting fluid dynamics and evolution has been a long-standing challenge in physical sciences. Conventional deep learning methods often rely on the nonlinear modeling…