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.
Existence results for energetic models for rate-independent systems.Calculus of Variations and Partial Differential Equations, 22(1):73–99, Jan 2005
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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.