In the present work, a machine learning based constitutive model for electro-mechanically
coupled material behavior at finite deformations is proposed. Using different sets of …

The integration of machine learning (Keplerian paradigm) and more general artificial
intelligence technologies with physical modeling based on first principles (Newtonian …

This paper aims to propose a data-driven computing algorithm integrated with model
reduction technique to conduct instability analysis of thin composite structures. The data …

A new method combining computational homogenization and the Artificial Neural Network
(ANN) is proposed to construct elastoplastic composites database efficiently for data-driven …

The distance-minimizing data-driven algorithm brings new insight in computing nonlinear
problems of composite structures, where the calculation is directly carried out by finding the …

This work presents a data‐driven magnetostatic finite‐element solver that is specifically well
suited to cope with strongly nonlinear material responses. The data‐driven computing …

The distance-minimizing data-driven computing is an emerging field of computational
mechanics, which reformulates the classical boundary-value problem as a discrete …

Abstract Design patterns provide a systematic way to convey solutions to recurring modeling
challenges. This paper introduces design patterns for hybrid modeling, an approach that …

We propose a randomized data-driven solver for multiscale mechanics problems which
improves accuracy by escaping local minima and reducing dependency on metric …

The distance-minimizing data-driven computational mechanics has great potential in
engineering applications by eliminating material modeling error and uncertainty. In this …