  This project aims to advance differentiable fluid dynamics for hypersonic
coupled flow over porous media, demonstrating the potential of automatic
differentiation (AD)-based optimization for end-to-end solutions. Leveraging AD
efficiently handles high-dimensional optimization problems, offering a flexible
alternative to traditional methods. We utilized JAX-Fluids, a newly developed
solver based on the JAX framework, which combines autograd and TensorFlow's
XLA. Compiled on a HAWK-AI node with NVIDIA A100 GPU, JAX-Fluids showed
computational performance comparable to other high-order codes like FLEXI.
Validation with a compressible turbulent channel flow DNS case showed excellent
agreement, and a new boundary condition for modeling porous media was
successfully tested on a laminar boundary layer case. Future steps in our
research are anticipated.
