Surrogate Models for Stress-Strain Mapping of Microscale Physics

•Learn the difference between different neural networks within machine learning (ML) •Develop a working understanding of the ML tool Pytorch and machine learning operator: Recurrent Neural Operator •Use MATLAB to create and process time dependent stress/strain matrices to display the hyper-parameters for different RNOs •Apply RNO to train the strain-stress mapping of tri-laminate and granular cases

Publication Details

Journal
DigitalCommons - CalPoly (California State Polytechnic University)
Published
2024-10-01
Primary Topic
Machine Learning in Materials Science
Type
article
Field-Weighted Citation Impact
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article

Surrogate Models for Stress-Strain Mapping of Microscale Physics

DigitalCommons - CalPoly (California State Polytechnic University)
Machine Learning in Materials Science
article

Surrogate Models for Stress-Strain Mapping of Microscale Physics

article en

Abstract

•Learn the difference between different neural networks within machine learning (ML) •Develop a working understanding of the ML tool Pytorch and machine learning operator: Recurrent Neural Operator •Use MATLAB to create and process time dependent stress/strain matrices to display the hyper-parameters for different RNOs •Apply RNO to train the strain-stress mapping of tri-laminate and granular cases

DigitalCommons - CalPoly (California State Polytechnic University)
Openalex Percentile: Top 67%
Machine Learning in Materials Science
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Surrogate Models for Stress-Strain Mapping of Microscale Physics · DigitalCommons - CalPoly (California State Polytechnic University) (2024) | TGRS Research Map | TGRS