Physics-Informed Neural Networks for the Structural Analysis and Monitoring of Railway Bridges: A Systematic Review

Physics-informed neural networks (PINNs) offer a mesh-free approach to solving partial differential equations (PDEs) with embedded physical constraints. Although PINNs have gained traction in various engineering fields, their adoption for railway bridge analysis remains under-explored. To address th...

תיאור מלא

מידע ביבליוגרפי

Principais autores:	Yuniel Martinez, Luis Rojas, Alvaro Peña, Matías Valenzuela, Jose Garcia
פורמט:	Artigo
שפה:	Inglês
יצא לאור:	MDPI AG 2025-05-01
סדרה:	Mathematics
נושאים:	physics-informed neural networks (PINNs) partial differential equations (PDEs) scientific machine learning domain decomposition inverse problems computational mechanics
גישה מקוונת:	https://www.mdpi.com/2227-7390/13/10/1571
תגים:	הוספת תג אין תגיות, היה/י הראשונ/ה לתייג את הרשומה!