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Quickstart tutorials

Getting started with jaxmat
Batching across material points
Batching across material parameters
Composable and differentiable material models

Examples

Phenomenological models
Data-driven identification
- Identification of a nonlinear isotropic hardening rule
- Identification of a nonlinear hardening model from cyclic data
Machine-learning based models
- Physics-Augmented Neural Networks for data-driven hyperelasticity
- Neural Generalized Standard Material for viscoelasticity

General resources

Nonlinear constitutive behaviors
Computational aspects
Conventions for representing tensors

API

jaxmat.loader module
jaxmat.materials package
jaxmat.solvers package
- jaxmat.solvers.custom_newton module
- jaxmat.solvers.custom_optimistix_solvers
jaxmat.state module
jaxmat.tensors package

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Data-driven identification

Data-driven identification#

Identification of a nonlinear isotropic hardening rule
Identification of a nonlinear hardening model from cyclic data

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Identification of a nonlinear isotropic hardening rule

By Jeremy Bleyer