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Titel:

WENO3-NN: A maximum-order three-point data-driven weighted essentially non-oscillatory scheme

Dokumenttyp:
Zeitschriftenaufsatz
Autor(en):
Bezgin, Deniz A.; Schmidt, Steffen J.; Adams, Nikolaus A.
Abstract:
Neural networks have become more and more relevant for computational fluid dynamics. In recent works, neural network based weighted essentially non-oscillatory schemes have been developed. Challenges faced with such schemes are to ensure maximum-order convergence on narrow stencils and the ENO property. In this work, we use a neural network as a weighting function in the WENO scheme and address these shortcomings. Based on the input stencil, the neural network calculates a convex combination of...     »
Stichworte:
WENO WENO-JS WENO-Z Neural network Machine learning Euler equations
Dewey Dezimalklassifikation:
620 Ingenieurwissenschaften
Horizon 2020:
grant agreement No. 667483
Zeitschriftentitel:
Journal of Computational Physics
Jahr:
2022
Band / Volume:
452
Seitenangaben Beitrag:
110920
Nachgewiesen in:
Scopus
Sprache:
en
Volltext / DOI:
doi:10.1016/j.jcp.2021.110920
Verlag / Institution:
Elsevier BV
E-ISSN:
0021-9991
Hinweise:
Funding text This project has received funding from the European Research Council (ERC) under the European Union's Horizon 2020 research and innovation programme (grant agreement No. 667483 ).
Eingereicht (bei Zeitschrift):
17.09.2021
Angenommen (von Zeitschrift):
22.12.2021
Publikationsdatum:
01.03.2022
TUM Einrichtung:
Lehrstuhl für Aerodynamik und Strömungsmechanik
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