In the framework of solid mechanics, the task of deriving material parameters from
experimental data has recently re-emerged with the progress in full-field measurement …

Thermodynamics could be seen as an expression of physics at a high epistemic level. As
such, its potential as an inductive bias to help machine learning procedures attain accurate …

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 …

Data-driven methods have changed the way we understand and model materials. However,
while providing unmatched flexibility, these methods have limitations such as reduced …

We extend EUCLID, a computational strategy for automated material model discovery and
identification, to linear viscoelasticity. For this case, we perform a priori model selection by …

We present a framework for the multiscale modeling of finite strain magneto-elasticity based
on physics-augmented neural networks (NNs). By using a set of problem specific invariants …

We develop a fully data-driven model of anisotropic finite viscoelasticity using neural
ordinary differential equations as building blocks. We replace the Helmholtz free energy …

Data-driven constitutive modeling with neural networks has received increased interest in
recent years due to its ability to easily incorporate physical and mechanistic constraints and …

The two fundamental concepts of materials theory, pseudo potentials and the assumption of
a multiplicative decomposition, allow a general description of inelastic material behavior …

The present study aims to introduce an AI algorithm suitable for neuromorphic computing to
solve Boundary Value Problems in Engineering Mechanics. Following the trend of …