The rapid growth of deep learning research, including within the field of computational
mechanics, has resulted in an extensive and diverse body of literature. To help researchers …

Abstract Machine learning methods have been widely applied to structural reliability
analysis, due to the excellent performance in modeling precision and efficiency. In this …

We propose a local, non-intrusive model order reduction technique to accurately
approximate the solution of coupled multi-component parametrized systems governed by …

This study explores reduced-order modeling for analyzing time-dependent diffusion-
deformation of hydrogels. The full-order model describing hydrogel transient behavior …

We present a finite elements—neural network approach for the numerical approximation of
parametric partial differential equations. The algorithm generates training data from finite …

Multi‐scale modeling allows the description of real reactive systems under industrially
relevant conditions. However, its application to rational catalyst and reactor design is …

The rapid growth of deep learning research, including within the field of computational
mechanics, has resulted in an extensive and diverse body of literature. To help researchers …

This paper is a contribution in the context of variational data assimilation combined with
statistical learning. The framework of data assimilation traditionally uses data collected at …

In this contribution, we develop an efficient surrogate modeling framework for simulation-
based optimization of enhanced oil recovery, where we particularly focus on polymer …

In the framework of reduced basis methods, we recently introduced a new certified
hierarchical and adaptive surrogate model, which can be used for efficient approximation of …