Physics informed neural networks for fluid flow analysis with repetitive parameter initialization

Abstract Physics-informed neural networks (PINNs) have been widely used to capture the behavior of physical systems governed by partial differential equations (PDEs), enabling the simulation of fluid dynamics across various scenarios. However, when applied to stiff fluid problems, the existing PINNs...

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Bibliographic Details
Main Authors: Jongmok Lee, Seungmin Shin, Taewan Kim, Bumsoo Park, Ho Choi, Anna Lee, Minseok Choi, Seungchul Lee
Format: Article
Language:English
Published: Nature Portfolio 2025-05-01
Series:Scientific Reports
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Online Access:https://doi.org/10.1038/s41598-025-99354-5
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