On physics-informed data-driven isotropic and anisotropic constitutive models through probabilistic machine learning and space-filling sampling

On physics-informed data-driven isotropic and anisotropic constitutive models through probabilistic machine learning and space-filling sampling

Data-driven constitutive modeling is an emerging field in computational solid mechanics with the prospect of significantly relieving the computational costs of hierarchical computational methods. Additionally, this data-driven paradigm could enable a seamless connection of experimental data probing...

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Bibliographic Details
Main Authors: Bouklas, N. (Author), Fuhg, J.N (Author)
Format: Article
Language:English
Published: Elsevier B.V. 2022
Subjects:
Anisotropy
Constitutive models
Data driven
Data-driven constitutive model
Data-driven constitutive models
Elasticity
Finite strain
Gaussian process regression
Hyperelasticity
Hyper-elasticity
Isotropics
Machine learning
Neural networks
Physic-informed machine learning
Physics-informed machine learning
Probabilistics
Simulated annealing
Solid mechanics
Space filling
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