Since their inception, computational homogenization methods based on the fast Fourier
transform (FFT) have grown in popularity, establishing themselves as a powerful tool …

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 …

We present a two-scale topology optimization framework for the design of macroscopic
bodies with an optimized elastic response, which is achieved by means of a spatially-variant …

Within the scope of our recent approach for Efficient Unsupervised Constitutive Law
Identification and Discovery (EUCLID), we propose an unsupervised Bayesian learning …

Abstract Machine learning has found its way into almost every area of science and
engineering, and we are only at the beginning of its exploration across fields. Being a …

Deciding whether a given function is quasiconvex is generally a difficult task. Here, we
discuss a number of numerical approaches that can be used in the search for a …

This paper presents an interpolation scheme applicable to meshless/mesh-based methods.
The formulation is capable of generating ordinary as well as non-negative shape functions …

For metals under fatigue, microplastic strain localisation leads to the formation of discrete
slip bands, which contributes to the initiation and propagation of short cracks. In this paper, a …

The numerical simulation of additive manufacturing techniques promises the acceleration of
costly experimental procedures to identify suitable process parameters. We recently …

This paper explores 2D lattice-type metamaterials featuring bi-stable unit cells and
predefined symmetry configurations. Our investigation delves into the emergence of phase …