A mechanics‐informed artificial neural network approach in data‐driven constitutive modeling

A mechanics‐informed artificial neural network approach for learning constitutive laws governing complex, nonlinear, elastic materials from strain–stress data is proposed. The approach features a robust and accurate method for training a regression‐based model capable of capturing highly nonlinear s...

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Veröffentlicht in:	International journal for numerical methods in engineering Jg. 123; H. 12; S. 2738 - 2759
Hauptverfasser:	As'ad, Faisal, Avery, Philip, Farhat, Charbel
Format:	Journal Article
Sprache:	Englisch
Veröffentlicht:	Hoboken, USA John Wiley & Sons, Inc 30.06.2022 Wiley Subscription Services, Inc
Schlagworte:	artificial neural network Artificial neural networks Computer architecture Computer simulation constitutive modeling convexity Dynamic stability Finite element method hyperelasticity Learning machine learning Mechanics Neural networks Noise sensitivity Parachute canopies Regression models Rigid structures Robustness (mathematics) Solid mechanics stability Strain supersonic parachute inflation dynamics Training Woven fabrics
ISSN:	0029-5981, 1097-0207
Online-Zugang:	Volltext
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