Bivariate B-spline surfaces on the invariant domain provide non-separable hyperelastic energy models that are calibrated instantaneously via linear least squares from homogeneous deformation stresses.
K.et al.Neural networks meet hyperelasticity: A monotonic approach (2025)
3 Pith papers cite this work. Polarity classification is still indexing.
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UNVERDICTED 3representative citing papers
Two extensions to the DAVIS framework improve robustness of non-equilibrium parameter identification in finite strain viscoelasticity via curvature-based splines and decoupled domain adaptation.
paFEMU enables rapid constitutive model discovery by integrating sparse regression, physics augmentation, and finite element adjoint optimization on multi-modal data for interpretable transfer learning.
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Data-adaptive spline surfaces for non-separable hyperelastic energy functions
Bivariate B-spline surfaces on the invariant domain provide non-separable hyperelastic energy models that are calibrated instantaneously via linear least squares from homogeneous deformation stresses.
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Learning finite viscoelasticity with DAVIS: A supervised framework for generalized standard materials
Two extensions to the DAVIS framework improve robustness of non-equilibrium parameter identification in finite strain viscoelasticity via curvature-based splines and decoupled domain adaptation.
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Towards Rapid Constitutive Model Discovery from Multi-Modal Data: Physics Augmented Finite Element Model Updating (paFEMU)
paFEMU enables rapid constitutive model discovery by integrating sparse regression, physics augmentation, and finite element adjoint optimization on multi-modal data for interpretable transfer learning.