A physics-informed operator regression framework for extracting data-driven continuum models

Performance and Cost Assessment of Machine Learning Interatomic Potentials

(2020) Yunxing Zuo et al.   JOURNAL OF PHYSICAL CHEMISTRY A

Data-driven deep learning of partial differential equations in modal space

(2020) Kailiang Wu et al.   JOURNAL OF COMPUTATIONAL PHYSICS

Numerical aspects for approximating governing equations using data

(2019) Kailiang Wu et al.   JOURNAL OF COMPUTATIONAL PHYSICS

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 modeling and learning in science and engineering

(2019) Francisco J. Montáns et al.   COMPTES RENDUS MECANIQUE

Data driven governing equations approximation using deep neural networks

(2019) Tong Qin et al.   JOURNAL OF COMPUTATIONAL PHYSICS

PDE-Net 2.0: Learning PDEs from data with a numeric-symbolic hybrid deep network

(2019) Zichao Long et al.   JOURNAL OF COMPUTATIONAL PHYSICS

Physics-informed machine learning approach for augmenting turbulence models: A comprehensive framework

(2018) Jin-Long Wu et al.   Physical Review Fluids

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

Data-driven discovery of partial differential equations

(2017) Samuel H. Rudy et al.   Science Advances

Machine learning strategies for systems with invariance properties

(2016) Julia Ling et al.   JOURNAL OF COMPUTATIONAL PHYSICS

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

Incorporation of memory effects in coarse-grained modeling via the Mori-Zwanzig formalism

(2015) Zhen Li et al.   JOURNAL OF CHEMICAL PHYSICS

Rotation-invariant convolutional neural networks for galaxy morphology prediction

(2015) Sander Dieleman et al.   MONTHLY NOTICES OF THE ROYAL ASTRONOMICAL SOCIETY

Deep learning

(2015) Yann LeCun et al.   NATURE

Compressive sensing based machine learning strategy for characterizing the flow around a cylinder with limited pressure measurements

(2013) Ido Bright et al.   PHYSICS OF FLUIDS

Inverse problems: A Bayesian perspective

(2010) A. M. Stuart   ACTA NUMERICA

Distilling Free-Form Natural Laws from Experimental Data

(2009) Michael Schmidt et al.   SCIENCE

A Newton-CG method for large-scale three-dimensional elastic full-waveform seismic inversion

(2008) I Epanomeritakis et al.   INVERSE PROBLEMS