Author

N. Bouklas

4 papers indexed here

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Preprint Aug 2026

Read, Write, Relax: Why Neural PDE Surrogates Need Both Global and Local Processing

Recent mesh-based simulation advances have, in no small part, relied on neural surrogates of two distinct families: global models that route information through a small set of latent tokens, and local models that perform message passing across mesh edges. Consistent with both classes is the inability to perform beyond...

Anuj Kumar, Heiko Zimmermann, J. Bjorgaard et al. · 0 citations

#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