Finite element method-enhanced neural network for forward and inverse problems

Finite element method-enhanced neural network for forward and inverse problems

Abstract We introduce a novel hybrid methodology that combines classical finite element methods (FEM) with neural networks to create a well-performing and generalizable surrogate model for forward and inverse problems. The residual from finite element methods and custom loss functions from neural ne...

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Bibliographic Details
Main Authors: Rishith E. Meethal, Anoop Kodakkal, Mohamed Khalil, Aditya Ghantasala, Birgit Obst, Kai-Uwe Bletzinger, Roland Wüchner
Format: Article
Language:English
Published: SpringerOpen 2023-05-01
Series:Advanced Modeling and Simulation in Engineering Sciences
Subjects:
Hybrid models
Informed machine learning
FEM-based neural network
Self-supervised learning
Online Access:https://doi.org/10.1186/s40323-023-00243-1

Internet

https://doi.org/10.1186/s40323-023-00243-1

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