Physics informed neural networks for continuum micromechanics

Machine learning for metal additive manufacturing: predicting temperature and melt pool fluid dynamics using physics-informed neural networks

(2021) Qiming Zhu et al.   COMPUTATIONAL MECHANICS

Physics-Informed Neural Networks for Heat Transfer Problems

(2021) Shengze Cai et al.   JOURNAL OF HEAT TRANSFER-TRANSACTIONS OF THE ASME

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

Learning hidden elasticity with deep neural networks

(2021) Chun-Teh Chen et al.   PROCEEDINGS OF THE NATIONAL ACADEMY OF SCIENCES OF THE UNITED STATES OF AMERICA

A deep learning driven pseudospectral PCE based FFT homogenization algorithm for complex microstructures

(2021) Alexander Henkes et al.   COMPUTER METHODS IN APPLIED MECHANICS AND ENGINEERING

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

(2020) Maziar Raissi et al.   SCIENCE

An energy approach to the solution of partial differential equations in computational mechanics via machine learning: Concepts, implementation and applications

(2020) E. Samaniego et al.   COMPUTER METHODS IN APPLIED MECHANICS AND ENGINEERING

Physics-informed neural networks for inverse problems in nano-optics and metamaterials

(2020) Yuyao Chen et al.   OPTICS EXPRESS

Application of the Finite Element Method in the Analysis of Composite Materials: A Review

(2020) Sarah David Müzel et al.   Polymers

The neural particle method – An updated Lagrangian physics informed neural network for computational fluid dynamics

(2020) Henning Wessels et al.   COMPUTER METHODS IN APPLIED MECHANICS AND ENGINEERING

Conservative physics-informed neural networks on discrete domains for conservation laws: Applications to forward and inverse problems

(2020) Ameya D. Jagtap et al.   COMPUTER METHODS IN APPLIED MECHANICS AND ENGINEERING

Deep learning for inverse problems in quantum mechanics

(2020) Victor Lantz et al.   INTERNATIONAL JOURNAL OF QUANTUM CHEMISTRY

A survey on deep learning in medicine: Why, how and when?

(2020) Francesco Piccialli et al.   Information Fusion

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

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 deep energy method for finite deformation hyperelasticity

(2019) Vien Minh Nguyen-Thanh et al.   EUROPEAN JOURNAL OF MECHANICS A-SOLIDS

A unified deep artificial neural network approach to partial differential equations in complex geometries

(2018) Jens Berg et al.   NEUROCOMPUTING

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

Deep learning

(2015) Yann LeCun et al.   NATURE