Symmetric Basis Convolutions for Learning Lagrangian Fluid Mechanics

arXiv:2403.16680v1 Announce Type: new
Abstract: Learning physical simulations has been an essential and central aspect of many recent research efforts in machine learning, particularly for Navier-Stokes-based fluid mechanics. Classic numerical solvers have traditionally been computationally expensive and challenging to use in inverse problems, whereas Neural solvers aim to address both concerns through machine learning. We propose a general formulation for continuous convolutions using separable basis functions as a superset of existing methods and evaluate a large set of basis functions …

arxiv cs.lg physics.comp-ph type