Author

N. Bouklas

3 papers indexed here

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#machine learning Jul 2026

Interval and fuzzy physics-augmented neural networks (iPANN and fPANN) for uncertainty quantification and propagation in constitutive modeling

The proposed framework provides a compact, physics-consistent route for distribution-free aleatoric uncertainty quantification in hyperelastic constitutive modeling, and propagation in downstream finite element simulations.

S. P. Singh, G. Padmanabha, Jing-Yang Tan et al. · arXiv.org · 0 citations

#machine learning Preprint Sep 2026

Learning Physics from an Imperfect Ancestor

Neural operators evaluate parametric partial differential equations cheaply but degrade sharply outside their training distribution. Physics-informed neural networks avoid dependence on labeled data, yet their optimization can be basin-fragile: when the governing residual admits multiple solutions, a PINN trained from...

S. Mousavi, T. Kadeethum, N. Bouklas et al. · 0 citations

Preprint Aug 2026

Mixture of Polyconvex Neural Potentials for Parametric Hyperelasticity: Towards Foundation Material Models

Hyperelastic constitutive models enable modeling large deformations in elastic solids. In common practice, a strain energy density function is prescribed in advance and model-specific parameters are calibrated from experiments. However, many applications require constitutive models for a family of related materials who...

Steven J. Yang, G. Padmanabha, D. T. Seidl et al. · 1 citation