We are performing the digital transition of industry, living the 4th industrial revolution,
building a new World in which the digital, physical and human dimensions are interrelated in …

In this paper, we review constitutive models for soft materials. We specifically focus on
physically based models accounting for hyperelasticity, visco-hyperelasticity, and damage …

In the context of science, the well-known adage" a picture is worth a thousand words" might
well be" a model is worth a thousand datasets." In this manuscript we introduce the SciML …

Deep learning applications have been thriving over the last decade in many different
domains, including computer vision and natural language understanding. The drivers for the …

Developing accurate dynamic models for various systems is crucial for optimization, control,
fault diagnosis, and prognosis. Recent advancements in information technologies and …

The various studies of partial differential equations (PDEs) are hot topics of mathematical
research. Among them, solving PDEs is a very important and difficult task. Since many …

The development of data-informed predictive models for dynamical systems is of
widespread interest in many disciplines. We present a unifying framework for blending …

We propose a novel approach to model viscoelasticity materials, where rate-dependent and
non-linear constitutive relationships are approximated with deep neural networks. We …

The advent of technology including big data has allowed machine learning technology to
strengthen its place in solving different science and engineering complex problems …

This work presents a physics-informed neural network (PINN) based framework to model the
strain-rate and temperature dependence of the deformation fields in elastic-viscoplastic …