A machine learning approach to automate ductile damage parameter selection using finite element simulations-Reference-Cited by-同舟云学术

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

Published:2024-01 Issue: Volume:103 Page:105180
ISSN:0997-7538
Container-title:European Journal of Mechanics - A/Solids
language:en
Short-container-title:European Journal of Mechanics - A/Solids

Author:

O’Connor A.N.^ORCID,Mongan P.G.,O’Dowd N.P.

Funder

European Commission

H2020 Marie Skłodowska-Curie Actions

Publisher

Elsevier BV

Subject

General Physics and Astronomy,Mechanical Engineering,Mechanics of Materials,General Materials Science

Reference50 articles.

1. Identification of GTN model parameters by application of response surface methodology;Abbasi;Procedia Eng.,2011

2. Parameter identification of a mechanical ductile damage using Artificial Neural Networks in sheet metal forming;Abbassi;Mater. Des.,2013

3. Poromechanics of fractured/faulted reservoirs during fluid injection based on continuum damage modeling and machine learning;Abbassi;Nat. Resour. Res.,2023

4. Identification of ductile damage and fracture parameters from the small punch test using neural networks;Abendroth;Eng. Fract. Mech.,2006

5. Fracture Mechanics: Fundamentals and Applications;Anderson,2017

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