Optimization framework for calibration of constitutive models enhanced by neural networks

DOI10.1002/nag.707zbMath1272.74463OpenAlexW2032702297MaRDI QIDQ2848328

Andrzej Truty, Rafał F. Obrzud, Laurent Vulliet

Publication date: 26 September 2013

Published in: International Journal for Numerical and Analytical Methods in Geomechanics (Search for Journal in Brave)

Full work available at URL: http://infoscience.epfl.ch/record/121378

zbMATH Keywords

optimizationneural networksprincipal component analysisparameter identificationback analysiselasto-plastic model calibration

Mathematics Subject Classification ID

Neural networks for/in biological studies, artificial life and related topics (92B20) Small-strain, rate-independent theories of plasticity (including rigid-plastic and elasto-plastic materials) (74C05) Soil and rock mechanics (74L10) Inverse problems in equilibrium solid mechanics (74G75) Optimization of other properties in solid mechanics (74P10)

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Statistical inverse analysis based on genetic algorithm and principal component analysis: Method and developments using synthetic data, Fully coupled poromechanical back analysis of the pulse test by inverse method, A combined neural network/gradient-based approach for the identification of constitutive model parameters using self-boring pressuremeter tests

Uses Software

• UCODE

Cites Work

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