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Titel:

Neural SPH: Improved Neural Modeling of Lagrangian Fluid Dynamics

Dokumenttyp:
Zeitschriftenaufsatz
Autor(en):
Toshev, Artur P.; Erbesdobler, Jonas A.; Adams, Nikolaus A.; Brandstetter, Johannes
Abstract:
Smoothed particle hydrodynamics (SPH) is omnipresent in modern engineering and scientific disciplines. SPH is a class of Lagrangian schemes that discretize fluid dynamics via finite material points that are tracked through the evolving velocity field. Due to the particle-like nature of the simulation, graph neural networks (GNNs) have emerged as appealing and successful surrogates. However, the practical utility of such GNN-based simulators relies on their ability to faithfully model physics, pr...     »
Dewey Dezimalklassifikation:
620 Ingenieurwissenschaften
Kongresstitel:
41st International Conference on Machine Learning
Kongress / Zusatzinformationen:
ICML 2024Vienna21 July 2024through 27 July 2024Code 201670
Zeitschriftentitel:
ML Research Press
Jahr:
2024
Band / Volume:
Volume 235
Seitenangaben Beitrag:
Volume 235, Pages 48428 - 48452
Nachgewiesen in:
Scopus
Sprache:
en
Volltext / DOI:
doi:10.48550/ARXIV.2402.06275
WWW:
https://www.scopus.com/record/display.uri?eid=2-s2.0-85203790650&origin=SingleRecordEmailAlert&dgcid=raven_sc_author_en_us_email&txGid=a6c7e37badb1c22a8abc4f0aa8b0df65
Verlag / Institution:
ML Research Press
Print-ISSN:
26403498
Publikationsdatum:
01.01.2024
TUM Einrichtung:
Lehrstuhl für Aerodynamik und Strömungsmechanik
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