Differentiable Fluid Dynamics in JAX: Challenges and Perspectives

presentation Aachen

Fri, 26 Aug 2022 10:00
Zoom

Deniz Bezgin (TUM), Aaron Buhendwa (TUM)

Differentiable Fluid Dynamics in JAX: Challenges and Perspectives

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" (https://arxiv.org/abs/2203.13760).
Physical systems are governed by partial differential equations (PDEs). The Navier-Stokes equations describe fluid flows and are representative of nonlinear physical systems with complex...

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

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