Deep learning predicts path-dependent plasticity

Deep elastic strain engineering of bandgap through machine learning

(2019) Zhe Shi et al.   PROCEEDINGS OF THE NATIONAL ACADEMY OF SCIENCES OF THE UNITED STATES OF AMERICA

Recent Trends in Deep Learning Based Natural Language Processing [Review Article]

(2018) Tom Young et al.   IEEE Computational Intelligence Magazine

Design of ultra-thin shell structures in the stochastic post-buckling range using Bayesian machine learning and optimization

(2018) M.A. Bessa et al.   INTERNATIONAL JOURNAL OF SOLIDS AND STRUCTURES

Combined molecular dynamics and neural network method for predicting protein antifreeze activity

(2018) Daniel J. Kozuch et al.   PROCEEDINGS OF THE NATIONAL ACADEMY OF SCIENCES OF THE UNITED STATES OF AMERICA

A Markov random field approach for modeling spatio-temporal evolution of microstructures

(2016) Pinar Acar et al.   MODELLING AND SIMULATION IN MATERIALS SCIENCE AND ENGINEERING

Understanding scaling through history-dependent processes with collapsing sample space

(2015) Bernat Corominas-Murtra et al.   PROCEEDINGS OF THE NATIONAL ACADEMY OF SCIENCES OF THE UNITED STATES OF AMERICA

A Descriptor-Based Design Methodology for Developing Heterogeneous Microstructural Materials System

(2014) Hongyi Xu et al.   JOURNAL OF MECHANICAL DESIGN

The high-throughput highway to computational materials design

(2013) Stefano Curtarolo et al.   NATURE MATERIALS

An alternative to kinematic hardening in classical plasticity

(2011) Frédéric Barlat et al.   INTERNATIONAL JOURNAL OF PLASTICITY

Numerically explicit potentials for the homogenization of nonlinear elastic heterogeneous materials

(2009) J. Yvonnet et al.   COMPUTER METHODS IN APPLIED MECHANICS AND ENGINEERING