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.
Journal of the Mechanics and Physics of Solids , volume =
8 Pith papers cite this work, alongside 147 external citations. Polarity classification is still indexing.
8
Pith papers citing it
147
external citations · external index
citation-role summary
background 1
citation-polarity summary
roles
background 1polarities
background 1representative citing papers
Polyconvexity implies true-stress-true-strain monotonicity in incompressible isotropic hyperelasticity, which is enforced in four PANN architectures that show varying extrapolation behavior on experimental data.
CSSV-NNs and inc-CSSV-NNs provide universal approximation of frame-indifferent isotropic polyconvex hyperelastic energies, showing Ball's criterion is sufficient but not necessary.
Input-convex neural networks in elementary polynomials of signed singular values provably approximate any frame-indifferent isotropic polyconvex hyperelastic energy.
A monotonic ICNN architecture with domain reduction to the positive octant approximates polyconvex envelopes of isotropic functions more efficiently than existing necessary-and-sufficient methods, demonstrated on Saint Venant-Kirchhoff energy.
A neural constitutive model with hard thermodynamic constraints learns inelastic stress-strain behavior from data and predicts unseen cyclic paths, including granular-media hysteresis.
EquiNO with Q-DEIM creates reduced-order physics-informed surrogates for 3D hyperelastic RVEs that enforce equilibrium and periodicity by construction, achieve 10^3 speedups, and accurately interpolate and extrapolate stresses from few snapshots.
Adaptive Material Fingerprinting builds hyperelastic material models as greedy linear combinations of precomputed feature fingerprints, reaching neural-network-level fit accuracy on rubber and skin without online optimization.
citing papers explorer
-
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.
-
Concurrent enforcement of polyconvexity and true-stress-true-strain monotonicity in incompressible isotropic hyperelasticity: application to neural network constitutive models
Polyconvexity implies true-stress-true-strain monotonicity in incompressible isotropic hyperelasticity, which is enforced in four PANN architectures that show varying extrapolation behavior on experimental data.
-
Modeling isotropic polyconvex hyperelasticity by neural networks -- sufficient and necessary criteria for compressible and incompressible materials
CSSV-NNs and inc-CSSV-NNs provide universal approximation of frame-indifferent isotropic polyconvex hyperelastic energies, showing Ball's criterion is sufficient but not necessary.
-
Input convex neural networks: universal approximation theorem and implementation for isotropic polyconvex hyperelastic energies
Input-convex neural networks in elementary polynomials of signed singular values provably approximate any frame-indifferent isotropic polyconvex hyperelastic energy.
-
Compression of Polyconvex Envelopes of Isotropic Functions via Monotonic Input Convex Neural Networks
A monotonic ICNN architecture with domain reduction to the positive octant approximates polyconvex envelopes of isotropic functions more efficiently than existing necessary-and-sufficient methods, demonstrated on Saint Venant-Kirchhoff energy.
-
Learning inelastic constitutive models from stress-strain data under hard thermodynamic constraints
A neural constitutive model with hard thermodynamic constraints learns inelastic stress-strain behavior from data and predicts unseen cyclic paths, including granular-media hysteresis.
-
Physics-Informed Reduced-Order Operator Learning for Hyperelasticity in Continuum Micromechanics
EquiNO with Q-DEIM creates reduced-order physics-informed surrogates for 3D hyperelastic RVEs that enforce equilibrium and periodicity by construction, achieve 10^3 speedups, and accurately interpolate and extrapolate stresses from few snapshots.
-
Adaptive Material Fingerprinting for the fast discovery of polyconvex feature combinations in isotropic and anisotropic hyperelasticity
Adaptive Material Fingerprinting builds hyperelastic material models as greedy linear combinations of precomputed feature fingerprints, reaching neural-network-level fit accuracy on rubber and skin without online optimization.