We present an approach for the data-driven modeling of nonlinear viscoelastic materials at
small strains which is based on physics-augmented neural networks (NNs) and requires …

The usage of numerical homogenization to obtain structure–property relations by applying
the finite element method at both the micro-and macroscale has gained much interest in the …

We present a data-driven framework for the multiscale modeling of anisotropic finite strain
elasticity based on physics-augmented neural networks (PANNs). Our approach allows the …

Metal/polymer-based composites hybrid (MPH) structures combine the high strength of
metals with the low density of polymer-based composites, making them widely used in …

The morphology of nanostructured materials exhibiting a polydisperse porous space, such
as aerogels, is very open porous and fine grained. Therefore, a simulation of the …

In the current digital landscape, artificial intelligence-driven automation has revolutionized
efficiency in various areas, enabling significant time and resource savings. However, the …

Multiscale problems are widely observed across diverse domains in physics and
engineering. Translating these problems into numerical simulations and solving them using …

Multiscale computations involving finite elements are often unfeasible due to their
substantial computational costs arising from numerous microstructure evaluations. This …

A neural network model for a constitutive law in nonlinear structures is proposed. The neural
model is constructed based on a data set of responses of representative volume elements …

This Special Issue comprises the second collection of papers submitted by both the Editorial
Board Members (EBMs) of the journal Mathematical and Computational Applications (MCA) …