For many decades, experimental solid mechanics has played a crucial role in characterizing
and understanding the mechanical properties of natural and novel artificial materials …

Over the past decade, there has been a significant surge in interest in flexible mechanical
force sensing devices and systems. Tremendous efforts have been devoted to the …

Computational material modeling using advanced numerical techniques speeds up the
design process and reduces the costs of developing new engineering products. In the field …

In the present work, advanced spatial and temporal discretization techniques are tailored to
hyperelastic physics-augmented neural networks, ie, neural network based constitutive …

This paper introduces a publicly available PyTorch-ABAQUS deep-learning framework of a
family of plasticity models where the yield surface is implicitly represented by a scalar …

This paper presents a model-free data-driven framework for breakage mechanics. In
contrast with continuum breakage mechanics, the de facto approach for the macroscopic …

This work presents a novel global digital image correlation (DIC) method, based on a newly
developed convolution finite element (C-FE) approximation. The convolution approximation …

As one of data-driven approaches to computational mechanics in elasticity, this paper
presents a method finding a bound for structural response, taking uncertainty in a material …

In this paper, a data-driven enhanced generalized differential quadrature (DE-GDQ)
algorithm in free vibration analysis is proposed, which can be applied to shells of revolution …

Abstract We study a Data‐Driven approach to inference in physical systems in a measure‐
theoretic framework. The systems under consideration are characterized by two measures …