This review article highlights state-of-the-art data-driven techniques to discover, encode,
surrogate, or emulate constitutive laws that describe the path-independent and path …

Abstract We present a Data-Driven framework for multiscale mechanical analysis of
materials. The proposed framework relies on the Data-Driven formulation in mechanics …

Analyzing and modeling the constitutive behavior of materials is a core area in materials
sciences and a prerequisite for conducting numerical simulations in which the material …

Data-driven constitutive modeling is an emerging field in computational solid mechanics
with the prospect of significantly relieving the computational costs of hierarchical …

We present a machine learning approach that integrates geometric deep learning and
Sobolev training to generate a family of finite strain anisotropic hyperelastic models that …

Gaussian process regression is a popular Bayesian framework for surrogate modeling of
expensive data sources. As part of a broader effort in scientific machine learning, many …

Experimental solid mechanics is at a pivotal point where machine learning (ML) approaches
are rapidly proliferating into the discovery process due to significant advances in data …

The development of highly accurate constitutive models for materials that undergo path-
dependent processes continues to be a complex challenge in computational solid …

Estimation of Newmark sliding displacement plays an important role for evaluating seismic
stability of slopes. Current empirical models generally utilize predefined functional forms and …

In this work we employ data-driven homogenization approaches to predict the particular
mechanical evolution of polycrystalline aggregates with tens of individual crystals. In these …