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Title:

JAX-Fluids: A fully-differentiable high-order computational fluid dynamics solver for compressible two-phase flows

Document type:
Zeitschriftenaufsatz
Author(s):
Bezgin, Deniz A.; Buhendwa, Aaron B.; Adams, Nikolaus A.
Abstract:
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 spatio-temporal interactions. Fluid flows are omnipresent in nature and engineering applications, and their accurate simulation is essential for providing insights into these processes. While PDEs are typically solved with numerical methods, the recent success of machine learning (ML) has shown that ML methods c...     »
Keywords:
Computational fluid dynamics; Differential programming; Level-set; Machine learning; Navier-Stokes equations; Turbulence; Two-phase flows
Dewey Decimal Classification:
620 Ingenieurwissenschaften
Journal title:
Computer Physics Communications
Year:
2023
Journal volume:
282
Pages contribution:
108527
Covered by:
Scopus
Language:
en
Fulltext / DOI:
doi:10.1016/j.cpc.2022.108527
Publisher:
Elsevier BV
E-ISSN:
0010-4655
Submitted:
20.04.2022
Accepted:
01.09.2022
Date of publication:
01.01.2023
TUM Institution:
Lehrstuhl für Aerodynamik und Strömungsmechanik
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