Additive manufacturing of industrially-relevant high-performance parts and products is today
a reality, especially for metal additive manufacturing technologies. The design complexity …

Artificial intelligence, especially machine learning (ML) and deep learning (DL) algorithms,
is becoming an important tool in the fields of materials and mechanical engineering …

For many decades, experimental solid mechanics has played a crucial role in characterizing
and understanding the mechanical properties of natural and novel artificial materials …

We extend the scope of our recently developed approach for unsupervised automated
discovery of material laws (denoted as EUCLID) to the general case of a material belonging …

Demand for the construction of new structures is increasing all over the world. Since the
construction sector dominates the global carbon footprint, new construction methods are …

Physics-informed neural networks (PINN) can solve partial differential equations (PDEs) by
encoding the mathematical information explicitly into the loss functions. In the context of …

This review article highlights state-of-the-art data-driven techniques to discover, encode,
surrogate, or emulate constitutive laws that describe the path-independent and path …

After a decade of periodic truss-, plate-, and shell-based architectures having dominated the
design of metamaterials, we introduce the non-periodic class of spinodoid topologies …

In this paper we introduce constitutive artificial neural networks (CANNs), a novel machine
learning architecture for data-driven modeling of the mechanical constitutive behavior of …

Constitutive modeling based on continuum mechanics theory has been a classical approach
for modeling the mechanical responses of materials. However, when constitutive laws are …