JAX-Fluids 2.0: Towards HPC for Differentiable CFD of Compressible Two-phase Flows

In our effort to facilitate machine learning-assisted computational fluid dynamics (CFD), we introduce the second iteration of JAX-Fluids. JAX-Fluids is a Python-based fully-differentiable CFD solver designed for compressible single- and two-phase flows. In this work, the first version is extended to incorporate high-performance computing (HPC) capabilities. We introduce a parallelization strategy utilizing JAX primitive operations that scales efficiently on GPU (up to 512 NVIDIA A100 graphics cards) and TPU (up to 1024 TPU v3 cores) HPC systems. We further demonstrate …

capabilities computational computing cs.ce cs.lg differentiable dynamics fluid dynamics hpc iteration jax machine machine learning performance physics.flu-dyn python solver work