Discovering uncertainty: Bayesian constitutive artificial neural networks

Cites Work

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The influence of the invariant \(I_8\) on the stress-deformation and ellipticity characteristics of doubly fiber-reinforced non-linearly elastic solids
Bayesian learning for neural networks
Constitutive artificial neural networks: a fast and general approach to predictive data-driven constitutive modeling by deep learning
Bayesian-EUCLID: discovering hyperelastic material laws with uncertainties
Bayesian neural networks for uncertainty quantification in data-driven materials modeling
Physics-informed neural networks: a deep learning framework for solving forward and inverse problems involving nonlinear partial differential equations
A theory of large elastic deformation.
Bayesian physics informed neural networks for real-world nonlinear dynamical systems
A new family of constitutive artificial neural networks towards automated model discovery
Automated discovery of generalized standard material models with EUCLID
Nonlinear solid mechanics. A continuum approach for engineering
LII. An essay towards solving a problem in the doctrine of chances. By the late Rev. Mr. Bayes, F. R. S. communicated by Mr. Price, in a letter to John Canton, A. M. F. R. S
Large elastic deformations of isotropic materials IV. further developments of the general theory
A new constitutive framework for arterial wall mechanics and a comparative study of material models
Automated model discovery for skin: discovering the best model, data, and experiment
The Elements of Statistical Learning
On sparse regression, \(L_p\)-regularization, and automated model discovery

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