Talk: Deniz Bezgin (TUM) and Aaron Buhendwa (TUM) about Differentiable Fluid Dynamics in JAX: Challenges and Perspectives, Friday, 26th August 2022, 10am CEST

JAX-FLUIDS is a CFD solver written in Python, which uses the JAX framework to enable automatic differentiation (AD). This allows one to easily create applications for data-driven simulations or other optimization problems.The talk is based on the recent preprint “JAX-FLUIDS: A fully-differentiable high-order computational fluid dynamics solver for compressible two-phase flows” (arXiv:2203.13760).

To obtain the Zoom link for this online talk, please get in touch with Gregor Gassner or Michael Schlottke-Lakemper.