A representative volume element network (RVE-net) for accelerating RVE analysis, microscale material identification, and defect characterization

A physics-informed deep learning framework for inversion and surrogate modeling in solid mechanics

(2021) Ehsan Haghighat et al.   COMPUTER METHODS IN APPLIED MECHANICS AND ENGINEERING

Mesoscopic and multiscale modelling in materials

(2021) Jacob Fish et al.   NATURE MATERIALS

Hidden fluid mechanics: Learning velocity and pressure fields from flow visualizations

(2020) Maziar Raissi et al.   SCIENCE

Hierarchical Deep Learning Neural Network (HiDeNN): An artificial intelligence (AI) framework for computational science and engineering

(2020) Sourav Saha et al.   COMPUTER METHODS IN APPLIED MECHANICS AND ENGINEERING

Physics-constrained deep learning for high-dimensional surrogate modeling and uncertainty quantification without labeled data

(2019) Yinhao Zhu et al.   JOURNAL OF COMPUTATIONAL PHYSICS

Exploring the 3D architectures of deep material network in data-driven multiscale mechanics

(2019) Zeliang Liu et al.   JOURNAL OF THE MECHANICS AND PHYSICS OF SOLIDS

Surrogate modeling for fluid flows based on physics-constrained deep learning without simulation data

(2019) Luning Sun 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

Coupling lattice structure topology optimization with design-dependent feature evolution for additive manufactured heat conduction design

(2018) Lin Cheng et al.   COMPUTER METHODS IN APPLIED MECHANICS AND ENGINEERING

Bayesian deep convolutional encoder–decoder networks for surrogate modeling and uncertainty quantification

(2018) Yinhao Zhu et al.   JOURNAL OF COMPUTATIONAL PHYSICS

Hidden physics models: Machine learning of nonlinear partial differential equations

(2018) Maziar Raissi et al.   JOURNAL OF COMPUTATIONAL PHYSICS

A deep material network for multiscale topology learning and accelerated nonlinear modeling of heterogeneous materials

(2018) Zeliang Liu et al.   COMPUTER METHODS IN APPLIED MECHANICS AND ENGINEERING

DGM: A deep learning algorithm for solving partial differential equations

(2018) Justin Sirignano et al.   JOURNAL OF COMPUTATIONAL PHYSICS

Physics-informed neural networks: A deep learning framework for solving forward and inverse problems involving nonlinear partial differential equations

(2018) M. Raissi et al.   JOURNAL OF COMPUTATIONAL PHYSICS

Mastering the game of Go without human knowledge

(2017) David Silver et al.   NATURE

Self-consistent clustering analysis: An efficient multi-scale scheme for inelastic heterogeneous materials

(2016) Zeliang Liu et al.   COMPUTER METHODS IN APPLIED MECHANICS AND ENGINEERING

Natural Language Processing in Radiology: A Systematic Review

(2016) Ewoud Pons et al.   RADIOLOGY

Reduced basis hybrid computational homogenization based on a mixed incremental formulation

(2013) Felix Fritzen et al.   COMPUTER METHODS IN APPLIED MECHANICS AND ENGINEERING

Routes for Efficient Computational Homogenization of Nonlinear Materials Using the Proper Generalized Decompositions

(2010) H. Lamari et al.   ARCHIVES OF COMPUTATIONAL METHODS IN ENGINEERING

Alleviating mesh constraints: Model reduction, parallel time integration and high resolution homogenization

(2007) F. Chinesta et al.   COMPUTER METHODS IN APPLIED MECHANICS AND ENGINEERING