Comparison of Neural FEM and Neural Operator Methods for applications in Solid Mechanics

Stefan Hildebrand; Sandra Klinge

doi:10.1007/s00521-024-10132-2

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arXiv · 2307.02494

Comparison of Neural FEM and Neural Operator Methods for applications in Solid Mechanics

Stefan Hildebrand·Sandra Klinge

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Abstract

Machine Learning methods belong to the group of most up-to-date approaches for solving partial differential equations. The current work investigates two classes, Neural FEM and Neural Operator Methods, for the use in elastostatics by means of numerical experiments. The Neural Operator methods require expensive training but then allow for solving multiple boundary value problems with the same Machine Learning model. Main differences between the two classes are the computational effort and accuracy. Especially the accuracy requires more research for practical applications.

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cs.CE cond-mat.mtrl-sci cs.NA cs.NE math.NA

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BibTeX RIS

Stefan Hildebrand, Sandra Klinge. 2023-07-04. Comparison of Neural FEM and Neural Operator Methods for applications in Solid Mechanics. https://doi.org/10.1007/s00521-024-10132-2

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