A convex neural network is trained inside an elastoplastic stress integration loop using force equilibrium losses to identify yield functions from full-field displacement data.
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A Bayesian optimal experimental design framework with Gaussian approximation of expected information gain and surrogate Fisher information enables optimized uniaxial tests that significantly improve identifiability of history-dependent constitutive parameters over random designs.
Recrystallization boundary migration in high-purity Al is modulated by the anisotropy of local internal stress states, with no observed shear-coupled motion.
citing papers explorer
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Physics-Informed Discovery of Yield Functions in Plasticity via Convex Neural Representations
A convex neural network is trained inside an elastoplastic stress integration loop using force equilibrium losses to identify yield functions from full-field displacement data.
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Optimal Experimental Design for Reliable Learning of History-Dependent Constitutive Laws
A Bayesian optimal experimental design framework with Gaussian approximation of expected information gain and surrogate Fisher information enables optimized uniaxial tests that significantly improve identifiability of history-dependent constitutive parameters over random designs.
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On stress-assisted boundary migration during recrystallization
Recrystallization boundary migration in high-purity Al is modulated by the anisotropy of local internal stress states, with no observed shear-coupled motion.