On the Identification of Material Constitutive Model Parameters Using Machine Learning Algorithms

This work aims to evaluate the predictive performance of various Machine Learning algorithms when applied to the prediction of material constitutive parameters, particularly the parameters of the Swift hardening law. For this, datasets were generated from the results of the numerical simulations of...

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Veröffentlicht in:	Key engineering materials 2022-07, Vol.926, p.2146-2153
Hauptverfasser:	Prates, Pedro A., Pereira, André, Ribeiro, Bernardete, Marques, Armando
Format:	Artikel
Sprache:	eng
Schlagworte:	Algorithms Constitutive models Decision trees Gaussian process Hardening Machine learning Metamodels Multilayer perceptrons Multilayers Parameter identification Performance prediction Process parameters Support vector machines Tensile tests
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