Proves H^{-1} norm equivalence to expectation over random test functions and introduces SV-PINNs that outperform standard PINNs on eight elliptic problems.
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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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Random test functions, $H^{-1}$ norm equivalence, and stochastic variational physics-informed neural networks
Proves H^{-1} norm equivalence to expectation over random test functions and introduces SV-PINNs that outperform standard PINNs on eight elliptic problems.
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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.