Solving Partial Differential Equations with Point Source Based on Physics-Informed Neural Networks

In recent years, deep learning technology has been used to solve partial differential equations (PDEs), among which the physics-informed neural networks (PINNs) emerges to be a promising method for solving both forward and inverse PDE problems. PDEs with a point source that is expressed as a Dirac d...

Ausführliche Beschreibung

Bibliographische Detailangaben

Veröffentlicht in:	arXiv.org 2021-11
Hauptverfasser:	Huang, Xiang, Liu, Hongsheng, Shi, Beiji, Wang, Zidong, Kang, Yang, Yang, Li, Weng, Bingya, Wang, Min, Chu, Haotian, Zhou, Jing, Fan, Yu, Hua, Bei, Chen, Lei, Dong, Bin
Format:	Artikel
Sprache:	eng
Schlagworte:	Accuracy Algorithms Artificial neural networks Continuity (mathematics) Deep learning Delta function Lower bounds Machine learning Mathematical models Neural networks Partial differential equations Probability density functions Singularities
Online-Zugang:	Volltext
Tags:	Tag hinzufügen Keine Tags, Fügen Sie den ersten Tag hinzu!