A paradigm for data-driven predictive modeling using field inversion and machine learning EJ Parish, K Duraisamy Journal of Computational Physics 305, 758-774, 2016 | 292 | 2016 |

*A priori* estimation of memory effects in reduced-order models of nonlinear systems using the Mori–Zwanzig formalismA Gouasmi, EJ Parish, K Duraisamy Proceedings of the Royal Society A: Mathematical, Physical and Engineering …, 2017 | 51* | 2017 |

Non-Markovian closure models for large eddy simulations using the Mori-Zwanzig formalism EJ Parish, K Duraisamy Phys. Rev. Fluids 2 (1), 014604, 2017 | 40 | 2017 |

A dynamic subgrid scale model for large eddy simulations based on the Mori–Zwanzig formalism EJ Parish, K Duraisamy Journal of Computational Physics 349, 154-175, 2017 | 39 | 2017 |

The Adjoint Petrov–Galerkin method for non-linear model reduction EJ Parish, CR Wentland, K Duraisamy Computer Methods in Applied Mechanics and Engineering 365, 112991, 2020 | 28* | 2020 |

Reduced order modeling of turbulent flows using statistical coarse-graining E Parish, K Duraisamy 46th AIAA Fluid Dynamics Conference, 3640, 2016 | 13 | 2016 |

A unified framework for multiscale modeling using the mori-zwanzig formalism and the variational multiscale method EJ Parish, K Duraisamy arXiv preprint arXiv:1712.09669, 2017 | 12 | 2017 |

Time-series machine-learning error models for approximate solutions to parameterized dynamical systems EJ Parish, KT Carlberg Computer Methods in Applied Mechanics and Engineering 365, 112990, 2020 | 11 | 2020 |

Quantification of turbulence modeling uncertainties using full field inversion E Parish, K Duraisamy 22nd AIAA Computational Fluid Dynamics Conference, 2459, 2015 | 10 | 2015 |

Generalized Riemann problem-based upwind scheme for the vorticity transport equations E Parish, K Duraisamy, P Chandrashekar Computers & Fluids 132, 10-18, 2016 | 8 | 2016 |

Windowed least-squares model reduction for dynamical systems EJ Parish, KT Carlberg Journal of Computational Physics 426, 109939, 2021 | 6 | 2021 |

Parameterized neural ordinary differential equations: Applications to computational physics problems K Lee, EJ Parish arXiv preprint arXiv:2010.14685, 2020 | 3 | 2020 |

Variational multiscale modeling and memory effects in turbulent flow simulations E Parish | 2 | 2018 |

Windowed space-time least-squares Petrov-Galerkin method for nonlinear model order reduction YS Shimizu, EJ Parish arXiv preprint arXiv:2012.06073, 2020 | 1 | 2020 |

Machine Learning Closure Modeling for Reduced-Order Models of Dynamical Systems. EJ Parish Sandia National Lab.(SNL-CA), Livermore, CA (United States), 2019 | 1 | 2019 |

A dynamic subgrid-scale model for LES based on the Mori-Zwanzig formalism E Parish, K Duraisamy Proceedings of the Summer Program, 275, 2016 | 1 | 2016 |

A compute-bound formulation of Galerkin model reduction for linear time-invariant dynamical systems F Rizzi, EJ Parish, PJ Blonigan, J Tencer Computer Methods in Applied Mechanics and Engineering 384, 113973, 2021 | | 2021 |

Enabling efficient uncertainty quantification for seismic modeling via projection-based model reduction F Rizzi, E Parish, P Blonigan, J Tencer EGU General Assembly Conference Abstracts, EGU21-4807, 2021 | | 2021 |

Model Reduction via Time-Continuous Least-Squares Residual Minimization E Parish APS Division of Fluid Dynamics Meeting Abstracts, L10. 002, 2019 | | 2019 |

Time-series Machine Learning Error Models for Appproximate Solutions to Dynamical Systems. EJ Parish, KT Carlberg Sandia National Lab.(SNL-CA), Livermore, CA (United States), 2019 | | 2019 |