Bayesian-EUCLID: Discovering hyperelastic material laws with uncertainties

Bayesian-EUCLID: Discovering hyperelastic material laws with uncertainties

Within the scope of our recent approach for Efficient Unsupervised Constitutive Law Identification and Discovery (EUCLID), we propose an unsupervised Bayesian learning framework for discovery of parsimonious and interpretable constitutive laws with quantifiable uncertainties. As in deterministic EUC...

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Bibliographic Details
Main Authors: De Lorenzis, L. (Author), Escande, M. (Author), Flaschel, M. (Author), Joshi, A. (Author), Kumar, S. (Author), Thakolkaran, P. (Author), Zheng, Y. (Author)
Format: Article
Language:English
Published: Elsevier B.V. 2022
Subjects:
Bayesian
Bayesian learning
Bayesian networks
Constitutive equations
Constitutive law
Constitutive modeling
Constitutive models
Data driven
Data-driven discovery
Domain Knowledge
Elasticity
Hierarchical Bayesian modeling
Hyperelasticity
Hyper-elasticity
Knowledge management
Machine learning
Monte Carlo methods
Probability distributions
Uncertainty
Uncertainty analysis
Uncertainty quantification
Uncertainty quantifications
Unsupervised learning
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