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 | 207* | 2019 |

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 | 177 | 2014 |

Hidden physics models: Machine learning of nonlinear partial differential equations M Raissi, GE Karniadakis Journal of Computational Physics 357, 125-141, 2018 | 152 | 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 | 100 | 2017 |

Deep hidden physics models: Deep learning of nonlinear partial differential equations M Raissi The Journal of Machine Learning Research 19 (1), 932-955, 2018 | 69 | 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 | 64 | 2017 |

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 | 57 | 2018 |

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 | 51 | 2018 |

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 | 51 | 2017 |

Forward-Backward Stochastic Neural Networks: Deep Learning of High-dimensional Partial Differential Equations M Raissi arXiv preprint arXiv:1804.07010, 2018 | 27 | 2018 |

Hidden fluid mechanics: A navier-stokes informed deep learning framework for assimilating flow visualization data M Raissi, A Yazdani, GE Karniadakis arXiv preprint arXiv:1808.04327, 2018 | 22 | 2018 |

Parametric Gaussian process regression for big data M Raissi, H Babaee, GE Karniadakis Computational Mechanics 64 (2), 409-416, 2019 | 18 | 2019 |

Deep learning of vortex-induced vibrations M Raissi, Z Wang, MS Triantafyllou, GE Karniadakis Journal of Fluid Mechanics 861, 119-137, 2019 | 17 | 2019 |

Deep multi-fidelity Gaussian processes M Raissi, G Karniadakis arXiv preprint arXiv:1604.07484, 2016 | 15 | 2016 |

Application of local improvements to reduced-order models to sampling methods for nonlinear PDEs with noise M Raissi, P Seshaiyer International Journal of Computer Mathematics 95 (5), 870-880, 2018 | 7* | 2018 |

A multi-fidelity stochastic collocation method for parabolic partial differential equations with random input data M Raissi, P Seshaiyer International Journal for Uncertainty Quantification 4 (3), 2014 | 7* | 2014 |

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 | 5 | 2019 |

Muti-fidelity Stochastic Collocation methods using Model Reduction techniques M Raissi | 1 | 2013 |

A deep-learning framework for inference in geomechanics E Haghighat, M Raissi, AM Rosende, H Gomez, R Juanes AGU Fall Meeting 2019, 2019 | | 2019 |

On parameter estimation approaches for predicting disease transmission through optimization, deep learning and statistical inference methods M Raissi, N Ramezani, P Seshaiyer Letters in Biomathematics, 1-26, 2019 | | 2019 |