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Found 4 papers, 1 papers with code
JAX-Fluids 2.0: Towards HPC for Differentiable CFD of Compressible Two-phase Flows
In our effort to facilitate machine learning-assisted computational fluid dynamics (CFD), we introduce the second iteration of JAX-Fluids.
JAX-FLUIDS: A fully-differentiable high-order computational fluid dynamics solver for compressible two-phase flows
AD in particular is essential to ML-CFD research as it provides gradient information and enables optimization of preexisting and novel CFD models.
A fully-differentiable compressible high-order computational fluid dynamics solver
While prior work has explored differentiable algorithms for one- or two-dimensional incompressible fluid flows, we present a fully-differentiable three-dimensional framework for the computation of compressible fluid flows using high-order state-of-the-art numerical methods.
Consistent and symmetry preserving data-driven interface reconstruction for the level-set method
We focus on interface reconstruction (IR) in the level-set method, i. e. the computation of the volume fraction and apertures.
