Inverse Modeling of Viscoelasticity Materials using Physics Constrained Learning

9 May 2020 Kailai Xu Alexandre M. Tartakovsky Jeff Burghardt Eric Darve

We propose a novel approach to model viscoelasticity materials using neural networks, which capture rate-dependent and nonlinear constitutive relations. However, inputs and outputs of the neural networks are not directly observable, and therefore common training techniques with input-output pairs for the neural networks are inapplicable... (read more)

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  • NUMERICAL ANALYSIS
  • NUMERICAL ANALYSIS