A nonlocal physics-informed deep learning framework using the peridynamic differential operator

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

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

(2020) Maziar Raissi et al.   SCIENCE

PPINN: Parareal physics-informed neural network for time-dependent PDEs

(2020) Xuhui Meng et al.   COMPUTER METHODS IN APPLIED MECHANICS AND ENGINEERING

SciANN: A Keras/TensorFlow wrapper for scientific computations and physics-informed deep learning using artificial neural networks

(2020) Ehsan Haghighat et al.   COMPUTER METHODS IN APPLIED MECHANICS AND ENGINEERING

hp-VPINNs: Variational physics-informed neural networks with domain decomposition

(2020) Ehsan Kharazmi et al.   COMPUTER METHODS IN APPLIED MECHANICS AND ENGINEERING

Learning data-driven discretizations for partial differential equations

(2019) Yohai Bar-Sinai et al.   PROCEEDINGS OF THE NATIONAL ACADEMY OF SCIENCES OF THE UNITED STATES OF AMERICA

Data-driven discovery of coordinates and governing equations

(2019) Kathleen Champion et al.   PROCEEDINGS OF THE NATIONAL ACADEMY OF SCIENCES OF THE UNITED STATES OF AMERICA

Perspective on machine learning for advancing fluid mechanics

(2019) M. P. Brenner et al.   Physical Review Fluids

Deep learning of aftershock patterns following large earthquakes

(2018) Phoebe M. R. DeVries et al.   NATURE

Solving high-dimensional partial differential equations using deep learning

(2018) Jiequn Han et al.   PROCEEDINGS OF THE NATIONAL ACADEMY OF SCIENCES OF THE UNITED STATES OF AMERICA

Machine Learning in Seismology: Turning Data into Insights

(2018) Qingkai Kong et al.   SEISMOLOGICAL RESEARCH LETTERS

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

Numerical solution of linear and nonlinear partial differential equations using the peridynamic differential operator

(2017) Erdogan Madenci et al.   NUMERICAL METHODS FOR PARTIAL DIFFERENTIAL EQUATIONS

Data-driven computational mechanics

(2016) T. Kirchdoerfer et al.   COMPUTER METHODS IN APPLIED MECHANICS AND ENGINEERING

Peridynamic differential operator and its applications

(2016) Erdogan Madenci et al.   COMPUTER METHODS IN APPLIED MECHANICS AND ENGINEERING

Deep learning

(2015) Yann LeCun et al.   NATURE

Convergence of Peridynamics to Classical Elasticity Theory

(2008) S. A. Silling et al.   JOURNAL OF ELASTICITY