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Titel:

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

Dokumenttyp:
Zeitschriftenaufsatz
Autor(en):
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...     »
Stichworte:
Computational fluid dynamics; Differential programming; Level-set; Machine learning; Navier-Stokes equations; Turbulence; Two-phase flows
Dewey Dezimalklassifikation:
620 Ingenieurwissenschaften
Zeitschriftentitel:
Computer Physics Communications
Jahr:
2023
Band / Volume:
282
Seitenangaben Beitrag:
108527
Nachgewiesen in:
Scopus
Sprache:
en
Volltext / DOI:
doi:10.1016/j.cpc.2022.108527
Verlag / Institution:
Elsevier BV
E-ISSN:
0010-4655
Eingereicht (bei Zeitschrift):
20.04.2022
Angenommen (von Zeitschrift):
01.09.2022
Publikationsdatum:
01.01.2023
TUM Einrichtung:
Lehrstuhl für Aerodynamik und Strömungsmechanik
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