Since the beginning of the industrial age, material performance and design have been in the
midst of innovation of many disruptive technologies. Today's electronics, space, medical …

This paper considers the creation of parametric surrogate models for applications in science
and engineering where the goal is to predict high-dimensional output quantities of interest …

Abstract An artificial Neural Network (NNW) is designed to serve as a surrogate model of
micro-scale simulations in the context of multi-scale analyses in solid mechanics. The …

An accurate assessment of the physical states of the Earth system is an essential component
of many scientific, societal, and economical considerations. These assessments are …

The present paper describes a method to enhance the capability of, or to broaden the scope
of computational mechanics by using deep learning, which is one of the machine learning …

Optimisation of manufacturing process parameters requires resource-intensive search in a
high-dimensional parameter space. In some cases, physics-based simulations can replace …

For short fiber reinforced plastic parts the local fiber orientation has a strong influence on the
mechanical properties. To enable multiscale computations using surrogate models we …

Data I/O poses a significant bottleneck in large-scale CFD simulations; thus, practitioners
would like to significantly reduce the number of times the solution is saved to disk, yet retain …

The role of intra-abdominal pressure (IAP) in spinal load reduction has remained
controversial, partly because previous musculoskeletal models did not introduce the …

This work introduces a method for learning low-dimensional models from data of high-
dimensional black-box dynamical systems. The novelty is that the learned models are …