A machine learning approach to automate ductile damage parameter selection using finite element simulations

DOI10.1016/J.EUROMECHSOL.2023.105180zbMath1530.74073OpenAlexW4388599374MaRDI QIDQ6141143

Noel P. O'Dowd, P. G. Mongan, A. N. O'Connor

Publication date: 2 January 2024

Published in: European Journal of Mechanics. A. Solids (Search for Journal in Brave)

Full work available at URL: https://doi.org/10.1016/j.euromechsol.2023.105180

zbMATH Keywords

non-unique solutionparameter selectionBayesian optimisationsecond test geometry

Mathematics Subject Classification ID

Learning and adaptive systems in artificial intelligence (68T05) Anelastic fracture and damage (74R20) Finite element methods applied to problems in solid mechanics (74S05) Numerical and other methods in solid mechanics (74S99)

Cites Work

• Machine learning-assisted parameter identification for constitutive models based on concatenated loading path sequences
• Algorithm 778: L-BFGS-B
• An enhanced data-driven constitutive model for predicting strain-rate and temperature dependent mechanical response of elastoplastic materials
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