Physics-informed neural networks: A deep learning framework for solving forward and inverse problems involving nonlinear partial differential equations M Raissi, P Perdikaris, GE Karniadakis Journal of Computational physics 378, 686-707, 2019 | 7171* | 2019 |

Hidden fluid mechanics: Learning velocity and pressure fields from flow visualizations M Raissi, A Yazdani, GE Karniadakis Science 367 (6481), 1026-1030, 2020 | 1228* | 2020 |

Hidden physics models: Machine learning of nonlinear partial differential equations M Raissi, GE Karniadakis Journal of Computational Physics 357, 125-141, 2018 | 1057 | 2018 |

Deep hidden physics models: Deep learning of nonlinear partial differential equations M Raissi The Journal of Machine Learning Research 19 (1), 932-955, 2018 | 730 | 2018 |

Machine learning of linear differential equations using Gaussian processes M Raissi, P Perdikaris, G Karniadakis Journal of Computational Physics 348 (Supplement C), 683 - 693, 2017 | 503 | 2017 |

A physics-informed deep learning framework for inversion and surrogate modeling in solid mechanics E Haghighat, M Raissi, A Moure, H Gomez, R Juanes Computer Methods in Applied Mechanics and Engineering 379, 113741, 2021 | 454* | 2021 |

Deep learning of vortex-induced vibrations M Raissi, Z Wang, MS Triantafyllou, GE Karniadakis Journal of Fluid Mechanics 861, 119-137, 2019 | 363 | 2019 |

Scientific machine learning through physics–informed neural networks: Where we are and what’s next S Cuomo, VS Di Cola, F Giampaolo, G Rozza, M Raissi, F Piccialli Journal of Scientific Computing 92 (3), 88, 2022 | 349 | 2022 |

The differential effects of oil demand and supply shocks on the global economy P Cashin, K Mohaddes, M Raissi, M Raissi Energy Economics 44, 113-134, 2014 | 322 | 2014 |

Nonlinear information fusion algorithms for data-efficient multi-fidelity modelling P Perdikaris, M Raissi, A Damianou, ND Lawrence, GE Karniadakis Proceedings of the Royal Society A: Mathematical, Physical and Engineering …, 2017 | 316 | 2017 |

Multistep Neural Networks for Data-driven Discovery of Nonlinear Dynamical Systems R Maziar, P Perdikaris, G Karniadakis arXiv preprint arXiv:1801.01236, https://arxiv.org/abs/1801.01236, 2018 | 304 | 2018 |

Numerical Gaussian processes for time-dependent and nonlinear partial differential equations M Raissi, P Perdikaris, GE Karniadakis SIAM Journal on Scientific Computing 40 (1), A172-A198, 2018 | 262 | 2018 |

Inferring solutions of differential equations using noisy multi-fidelity data M Raissi, P Perdikaris, GE Karniadakis Journal of Computational Physics 335, 736-746, 2017 | 251 | 2017 |

Forward-Backward Stochastic Neural Networks: Deep Learning of High-dimensional Partial Differential Equations M Raissi arXiv preprint arXiv:1804.07010, 2018 | 177 | 2018 |

Systems biology informed deep learning for inferring parameters and hidden dynamics A Yazdani, L Lu, M Raissi, GE Karniadakis PLoS computational biology 16 (11), e1007575, 2020 | 174 | 2020 |

Deep multi-fidelity Gaussian processes M Raissi, G Karniadakis arXiv preprint arXiv:1604.07484, 2016 | 63 | 2016 |

Deep learning of turbulent scalar mixing M Raissi, H Babaee, P Givi Physical Review Fluids 4 (12), 124501, 2019 | 61 | 2019 |

Machine learning of space-fractional differential equations M Gulian, M Raissi, P Perdikaris, G Karniadakis SIAM Journal on Scientific Computing 41 (4), A2485-A2509, 2019 | 60 | 2019 |

Physics informed deep learning (part i): Data-driven solutions of nonlinear partial differential equations. arXiv 2017 M Raissi, P Perdikaris, GE Karniadakis arXiv preprint arXiv:1711.10561, 0 | 54 | |

Parametric Gaussian process regression for big data M Raissi, H Babaee, GE Karniadakis Computational Mechanics 64, 409-416, 2019 | 47 | 2019 |