JAX-SPH: A Differentiable Smoothed Particle Hydrodynamics Framework

arXiv:2403.04750v1 Announce Type: cross
Abstract: Particle-based fluid simulations have emerged as a powerful tool for solving the Navier-Stokes equations, especially in cases that include intricate physics and free surfaces. The recent addition of machine learning methods to the toolbox for solving such problems is pushing the boundary of the quality vs. speed tradeoff of such numerical simulations. In this work, we lead the way to Lagrangian fluid simulators compatible with deep learning frameworks, and propose JAX-SPH - a Smoothed Particle …

arxiv cs.lg differentiable framework jax particle physics.flu-dyn type