Diff-FlowFSI: A GPU-accelerated, JAX-based Differentiable CFD Solver for Turbulent Flow and Fluid-Structure Interactions

Recent advances in deep learning (DL) have paved the way to develop neural models by integrating physics and deep learning techniques in a hybrid framework. A promising approach is to merge DL with established physics-based numerical solvers in a more integrated manner using differentiable programming, directly enhancing the solver's capability to handle complex dynamics by introducing DL-derived insights into the simulation process. Such an approach requires a solver capable of harnessing automatic differentiation. In this paper, we introduce a highly efficient GPU-accelerated JAX-based differentiable solver, Diff-FlowFSI, for simulating large-scale turbulent flows and flow-structure interactions. Using differentiable programming, finite volume method and immersed boundary method, Diff-FlowFSI offers three key features: efficient forward simulations with GPU accelerations, differentiability for space and time derivative extraction for inverse problems, and seamless integration with DL architectures. Through various benchmarks, we demonstrate the efficacy of Diff-FlowFSI in facilitating computational mechanics research at the DL-CFD interface.