User: Guest  Login
Title:

JAX-Fluids 2.0: Towards HPC for Differentiable CFD of Compressible Two-phase Flows

Document type:
Zeitschriftenaufsatz
Author(s):
Bezgin, Deniz A.; Buhendwa, Aaron B.; Adams, Nikolaus A.
Abstract:
In our effort to facilitate machine learning-assisted computational fluid dynamics (CFD), we introduce the second iteration of JAX-Fluids. JAX-Fluids is a Python-based fully-differentiable CFD solver designed for compressible single- and two-phase flows. In this work, the first version is extended to incorporate high-performance computing (HPC) capabilities. We introduce a parallelization strategy utilizing JAX primitive operations that scales efficiently on GPU (up to 512 NVIDIA A100 graphics c...     »
Keywords:
Computational fluid dynamics; Differential programming; Diffuse-interface; High performance computing; JAX; Level-set; Machine learning; Navier-Stokes equations; Turbulence; Two-phase flows
Dewey Decimal Classification:
620 Ingenieurwissenschaften
Journal title:
arXivLabs
Year:
2024
Covered by:
Scopus
Language:
en
Fulltext / DOI:
doi:10.48550/ARXIV.2402.05193
Publisher:
arXiv
Date of publication:
01.01.2024
TUM Institution:
Lehrstuhl für Aerodynamik und Strömungsmechanik
 BibTeX