Interval and fuzzy physics-augmented neural networks (iPANN and fPANN) for uncertainty quantification and propagation in constitutive modeling
Constitutive modeling under uncertainty remains a central challenge for reliable mechanics simulations, particularly when the available stress-deformation data are sparse, noisy, or heterogeneous. We propose interval and fuzzy physics-augmented neural networks (iPANNs and fPANNs) for uncertainty-aware hyperelastic constitutive modeling. iPANNs learn sparse lower, mean, and upper free energy density branches whose stresses, obtained by automatic differentiation, ultimately enclose noisy stress observations. In contrast to this deterministic interval description, fPANNs embed the learned iPANN b
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- PossiblePossibly related (embedding) · 47%Principled approaches for extending neural architectures to function spaces for operator learning →
- LinkedLinked via arxiv author · 85%Somesh Pratap Singh →
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- LinkedLinked via arxiv author · 85%Govinda Anantha Padmanabha →
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- LinkedLinked via arxiv author · 85%Jingye Tan →
“Interval and fuzzy physics-augmented neural networks (iPANN and fPANN) for uncertainty quantification and propagation in”
- LinkedLinked via arxiv author · 85%Steven Yang →
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- LinkedLinked via arxiv author · 85%Reese E. Jones →
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- LinkedLinked via arxiv author · 85%D. Thomas Seidl →
“Interval and fuzzy physics-augmented neural networks (iPANN and fPANN) for uncertainty quantification and propagation in”
- LinkedLinked via arxiv author · 85%Nikolaos Bouklas →
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