Physics-Informed Neural Networks: Current Progress and Challenges in Computational Solid and Structural Mechanics

Computer Modeling in Engineering & Sciences 2026 Open access

Read full text
Authors: Itthidet Thawon, Duy Vo, Tinh Quoc Bui, Kanya Rattanamongkhonkun, Chakkapong Chamroon, Nakorn Tippayawong, Yuttana Mona, Ramnarong Wanison, Pana Suttakul
DOI: 10.32604/cmes.2026.077044
Cited by: 5

Abstract

Physics-informed neural networks (PINNs) have emerged as a promising class of scientific machine learning techniques that integrate governing physical laws into neural network training. Their ability to enforce differential e... | Find, read and cite all the research you need on Tech Science Press

Fields of Study & Subjects

Model Reduction and Neural Networks Machine Learning in Materials Science Topology Optimization in Engineering

Publication details

Journal / source
Computer Modeling in Engineering & Sciences
Publisher
Tech Science Press
Volume
146
Issue
2
Pages
1–10
Publication date
1 January 2026
Language
EN