N-GINNs learn thermodynamically consistent GENERIC dynamics with non-quadratic dissipation potentials from data.
Theory and implementation of inelastic constitutive artificial neural networks.Computer Methods in Applied Mechanics and Engineering, 428:117063
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A framework using input convex neural networks to represent internal energy and dissipation potential for discovering thermomechanical constitutive models while guaranteeing thermodynamic admissibility by construction.
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Nonlinear GENERIC Informed Neural Networks (N-GINNs): learning GENERIC dynamics with non-quadratic dissipation potentials
N-GINNs learn thermodynamically consistent GENERIC dynamics with non-quadratic dissipation potentials from data.
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A Convex Route to Thermomechanics: Learning Internal Energy and Dissipation
A framework using input convex neural networks to represent internal energy and dissipation potential for discovering thermomechanical constitutive models while guaranteeing thermodynamic admissibility by construction.