Benutzer: Gast  Login
Dokumenttyp:
Zeitschriftenaufsatz
Autor(en):
Toshev, Artur P.; Erbesdobler, Jonas A.; Adams, Nikolaus A.; Brandstetter, Johannes
Titel:
Neural SPH: Improved Neural Modeling of Lagrangian Fluid Dynamics
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
Zeitschriftentitel:
arXivLabs
Jahr:
2024
Nachgewiesen in:
Scopus
Sprache:
en
Volltext / DOI:
doi:10.48550/ARXIV.2402.06275
WWW:
http://www.scopus.com/inward/record.url?eid=2-s2.0-85185867152&partnerID=MN8TOARS+
Verlag / Institution:
arXiv
Publikationsdatum:
01.01.2024
TUM Einrichtung:
Lehrstuhl für Aerodynamik und Strömungsmechanik
 BibTeX