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
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
A three-stage framework is introduced that freezes the spatial basis of a physics-informed NO, extrapolates its solution branch to an out-of-distribution parameter using a polynomial continuation prior, and distills the resulting field into a fresh PINN, establishing a simple principle: an NO need not accurately predic...
S. Mousavi, T. Kadeethum, Nikolaos Bouklas et al.· 0 citations
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
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