A sequential meta-transfer (SMT) learning to combat complexities of physics-informed neural networks: Application to composites autoclave processing-Reference-Cited by-同舟云学术

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A sequential meta-transfer (SMT) learning to combat complexities of physics-informed neural networks: Application to composites autoclave processing

Published:2024-08 Issue: Volume:283 Page:111597
ISSN:1359-8368
Container-title:Composites Part B: Engineering
language:en
Short-container-title:Composites Part B: Engineering

Author:

Ramezankhani Milad,Milani Abbas S.^ORCID

Funder

NSERC

Publisher

Elsevier BV

Reference49 articles.

1. Physics-informed neural networks: a deep learning framework for solving forward and inverse problems involving nonlinear partial differential equations;Raissi;J Comput Phys,2019

2. Uncertainty quantification in scientific machine learning: methods, metrics, and comparisons;Psaros,2022

3. Scientific machine learning through physics–informed neural networks: where we are and what's next;Cuomo;J Sci Comput,2022

4. A novel sequential method to train physics informed neural networks for Allen Cahn and Cahn Hilliard equations;Mattey;Comput Methods Appl Mech Eng,2022

5. PPINN: parareal physics-informed neural network for time-dependent PDEs;Meng;Comput Methods Appl Mech Eng,2020

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