Abstract Machine learning (ML) is increasing in popularity in the field of weather and climate
modelling. Applications range from improved solvers and preconditioners, to …

Neural networks (NNs) are currently changing the computational paradigm on how to
combine data with mathematical laws in physics and engineering in a profound way …

Data-driven constitutive modeling is an emerging field in computational solid mechanics
with the prospect of significantly relieving the computational costs of hierarchical …

The introduction of Physics-informed Neural Networks (PINNs) has led to an increased
interest in deep neural networks as universal approximators of PDEs in the solid mechanics …

The formulation of history-dependent material laws has been a significant research and
industrial activity in solid mechanics for over a century. A large variety of models has been …

The development of highly accurate constitutive models for materials that undergo path-
dependent processes continues to be a complex challenge in computational solid …

Spiking neural networks (SNN), also often referred to as the third generation of neural
networks, carry the potential for a massive reduction in memory and energy consumption …

The influence of the microstructure of a polycrystalline material on its macroscopic
deformation response is still one of the major problems in materials engineering. For …

Abstract The development of Physics-Informed Neural Networks (PINNs) over the recent
years has offered a promising avenue for the solution of partial differential equations, as well …

The first-passage failure of structural dynamic systems is a typical failure mode in
engineering. Most of the existing methods use ordinary or partial differential equations …