  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 the stable parallel
computation of automatic differentiation gradients across extended integration
trajectories. The new code version offers enhanced two-phase flow modeling
capabilities. In particular, a five-equation diffuse-interface model is
incorporated which complements the level-set sharp-interface model. Additional
algorithmic improvements include positivity-preserving limiters for increased
robustness, support for stretched Cartesian meshes, refactored I/O handling,
comprehensive post-processing routines, and an updated list of state-of-the-art
high-order numerical discretization schemes. We verify newly added numerical
models by showcasing simulation results for single- and two-phase flows,
including turbulent boundary layer and channel flows, air-helium shock bubble
interactions, and air-water shock drop interactions.
