Machine learning materials physics: Multi-resolution neural networks learn the free energy and nonlinear elastic response of evolving microstructures

Predicting the effective mechanical property of heterogeneous materials by image based modeling and deep learning

(2019) Xiang Li et al.   COMPUTER METHODS IN APPLIED MECHANICS AND ENGINEERING

A Review of the Application of Machine Learning and Data Mining Approaches in Continuum Materials Mechanics

(2019) Frederic E. Bock et al.   Frontiers in Materials

Machine learning materials physics: Integrable deep neural networks enable scale bridging by learning free energy functions

(2019) G.H. Teichert et al.   COMPUTER METHODS IN APPLIED MECHANICS AND ENGINEERING

Variational system identification of the partial differential equations governing the physics of pattern-formation: Inference under varying fidelity and noise

(2019) Z. Wang et al.   COMPUTER METHODS IN APPLIED MECHANICS AND ENGINEERING

Material structure-property linkages using three-dimensional convolutional neural networks

(2018) Ahmet Cecen et al.   ACTA MATERIALIA

Deep learning approaches for mining structure-property linkages in high contrast composites from simulation datasets

(2018) Zijiang Yang et al.   COMPUTATIONAL MATERIALS SCIENCE

Unconditionally stable, second-order schemes for gradient-regularized, non-convex, finite-strain elasticity modeling martensitic phase transformations

(2018) K. Sagiyama et al.   COMPUTER METHODS IN APPLIED MECHANICS AND ENGINEERING

A multiscale multi-permeability poroplasticity model linked by recursive homogenizations and deep learning

(2018) Kun Wang et al.   COMPUTER METHODS IN APPLIED MECHANICS AND ENGINEERING

A data-driven computational homogenization method based on neural networks for the nonlinear anisotropic electrical response of graphene/polymer nanocomposites

(2018) Xiaoxin Lu et al.   COMPUTATIONAL MECHANICS

Machine learning materials physics: Surrogate optimization and multi-fidelity algorithms predict precipitate morphology in an alternative to phase field dynamics

(2018) Gregory H. Teichert et al.   COMPUTER METHODS IN APPLIED MECHANICS AND ENGINEERING

Meta-modeling game for deriving theory-consistent, microstructure-based traction–separation laws via deep reinforcement learning

(2018) Kun Wang et al.   COMPUTER METHODS IN APPLIED MECHANICS AND ENGINEERING

A review of predictive nonlinear theories for multiscale modeling of heterogeneous materials

(2017) Karel Matouš et al.   JOURNAL OF COMPUTATIONAL PHYSICS

Bypassing the Kohn-Sham equations with machine learning

(2017) Felix Brockherde et al.   Nature Communications

Machine learning in materials informatics: recent applications and prospects

(2017) Rampi Ramprasad et al.   npj Computational Materials

Aspects of Computational Homogenization at Finite Deformations: A Unifying Review From Reuss' to Voigt's Bound

(2016) Saba Saeb et al.   Applied Mechanics Reviews

Unconditionally stable, second-order accurate schemes for solid state phase transformations driven by mechano-chemical spinodal decomposition

(2016) K. Sagiyama et al.   COMPUTER METHODS IN APPLIED MECHANICS AND ENGINEERING

PetIGA: A framework for high-performance isogeometric analysis

(2016) L. Dalcin et al.   COMPUTER METHODS IN APPLIED MECHANICS AND ENGINEERING

A three dimensional field formulation, and isogeometric solutions to point and line defects using Toupin's theory of gradient elasticity at finite strains

(2016) Z. Wang et al.   JOURNAL OF THE MECHANICS AND PHYSICS OF SOLIDS

Discovering governing equations from data by sparse identification of nonlinear dynamical systems

(2016) Steven L. Brunton et al.   PROCEEDINGS OF THE NATIONAL ACADEMY OF SCIENCES OF THE UNITED STATES OF AMERICA

Mechanochemical spinodal decomposition: a phenomenological theory of phase transformations in multi-component, crystalline solids

(2016) Shiva Rudraraju et al.   npj Computational Materials

A general-purpose machine learning framework for predicting properties of inorganic materials

(2016) Logan Ward et al.   npj Computational Materials

Computational homogenization of nonlinear elastic materials using neural networks

(2015) B. A. Le et al.   INTERNATIONAL JOURNAL FOR NUMERICAL METHODS IN ENGINEERING

Three-dimensional isogeometric solutions to general boundary value problems of Toupin’s gradient elasticity theory at finite strains

(2014) S. Rudraraju et al.   COMPUTER METHODS IN APPLIED MECHANICS AND ENGINEERING

Combinatorial screening for new materials in unconstrained composition space with machine learning

(2014) B. Meredig et al.   PHYSICAL REVIEW B

Multiscale methodology for bone remodelling simulation using coupled finite element and neural network computation

(2010) Ridha Hambli et al.   Biomechanics and Modeling in Mechanobiology

A Survey on Transfer Learning

(2009) Sinno Jialin Pan et al.   IEEE TRANSACTIONS ON KNOWLEDGE AND DATA ENGINEERING

Multi-scale computational homogenization: Trends and challenges

(2009) M.G.D. Geers et al.   JOURNAL OF COMPUTATIONAL AND APPLIED MATHEMATICS