A parameter-driven physics-informed neural network framework for solving two-parameter singular perturbation problems involving boundary layers

In this article, our goal is to solve two-parameter singular perturbation problems (SPPs) in one- and two-dimensions using an adapted Physics-Informed Neural Networks (PINNs) approach. Such problems are of major importance in engineering and the sciences, as they arise in control theory, fluid and g...

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Veröffentlicht in:	Advances in Computational Science and Engineering Jg. 5; S. 72 - 102
Hauptverfasser:	Boro, Pradanya, Raina, Aayushman, Natesan, Srinivasan
Format:	Journal Article
Sprache:	Englisch
Veröffentlicht:	01.09.2025
Schlagworte:	parameter asymptotic strategy two-parameter problem generalization error Physics-informed neural networks singular perturbation problem
ISSN:	2837-1739, 2837-1739
Online-Zugang:	Volltext
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