CFDBench: A Large-Scale Benchmark for Machine Learning Methods in Fluid Dynamics

arXiv:2310.05963v2 Announce Type: replace
Abstract: In recent years, applying deep learning to solve physics problems has attracted much attention. Data-driven deep learning methods produce fast numerical operators that can learn approximate solutions to the whole system of partial differential equations (i.e., surrogate modeling). Although these neural networks may have lower accuracy than traditional numerical methods, they, once trained, are orders of magnitude faster at inference. Hence, one crucial feature is that these operators can generalize to unseen PDE parameters without …

abstract arxiv attention benchmark cs.lg data data-driven deep learning differential dynamics fluid dynamics learn machine machine learning modeling networks neural networks numerical operators physics physics.comp-ph physics.flu-dyn scale solutions solve type