On automated model discovery and a universal material subroutine for hyperelastic materials

Constitutive modeling is the cornerstone of computational and structural mechanics. In a finite element analysis, the constitutive model is encoded in the material subroutine, a function that maps local strains onto stresses. This function is called within every finite element, at each integration point, within every time step, at each Newton...

NN-EUCLID: Deep-learning hyperelasticity without stress data

We propose a new approach for unsupervised learning of hyperelastic constitutive laws with physics-consistent deep neural networks. In contrast to supervised learning, which assumes the availability of stress–strain pairs, the approach only uses realistically measurable full-field displacement and global reaction force data, thus it lies...

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 EUCLID, we do not resort to stress data, but...

Coupled hydro-mechanical analysis of expansive soils: Parametric identification and calibration

A methodology for identifying and calibrating the material parameters for a coupled hydro-mechanical problem is presented in this paper. For validation purpose, a laboratory-based water infiltration test was numerically simulated using finite element method (FEM). The test was conducted using a self-designed column-type experimental device,...