A causal energetic neural network framework learns thermodynamically consistent history-dependent constitutive laws, proving internal variables are unique up to linear transformation and achieving 2% error on polycrystalline magnesium data.
Global existence results for viscoplasticity at finite strain.Archive for Rational Mechanics and Analysis, 227(1):423–475, Jan 2018
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Global existence of weak solutions is established for one-dimensional quasistatic nonlinear viscoelasticity with Bhattacharya-like viscosity, with solutions characterized as curves of maximal slope and satisfying a metric evolutionary variational inequality under convexity.
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A Neural-Network Framework to Learn History-Dependent Constitutive Laws and Identifiability of Internal Variables
A causal energetic neural network framework learns thermodynamically consistent history-dependent constitutive laws, proving internal variables are unique up to linear transformation and achieving 2% error on polycrystalline magnesium data.
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Gradient-flow characterizations of one-dimensional quasistatic viscoelasticity with Bhattacharya-like viscosity
Global existence of weak solutions is established for one-dimensional quasistatic nonlinear viscoelasticity with Bhattacharya-like viscosity, with solutions characterized as curves of maximal slope and satisfying a metric evolutionary variational inequality under convexity.