Arbitrarily high-order accurate entropy stable essentially nonoscillatory schemes for systems of conservation laws US Fjordholm, S Mishra, E Tadmor SIAM Journal on Numerical Analysis 50 (2), 544-573, 2012 | 317 | 2012 |

Estimates on the generalization error of physics-informed neural networks for approximating a class of inverse problems for PDEs S Mishra, R Molinaro IMA Journal of Numerical Analysis 42 (2), 981-1022, 2022 | 273 | 2022 |

Error estimates for deeponets: A deep learning framework in infinite dimensions S Lanthaler, S Mishra, GE Karniadakis Transactions of Mathematics and Its Applications 6 (1), tnac001, 2022 | 240 | 2022 |

On universal approximation and error bounds for Fourier neural operators N Kovachki, S Lanthaler, S Mishra Journal of Machine Learning Research 22 (290), 1-76, 2021 | 235 | 2021 |

Estimates on the generalization error of physics-informed neural networks for approximating PDEs S Mishra, R Molinaro IMA Journal of Numerical Analysis 43 (1), 1-43, 2023 | 217 | 2023 |

Optimal entropy solutions for conservation laws with discontinuous flux-functions ADIMURTHI, S Mishra, GDV Gowda Journal of Hyperbolic Differential Equations 2 (04), 783-837, 2005 | 208 | 2005 |

Well-balanced and energy stable schemes for the shallow water equations with discontinuous topography US Fjordholm, S Mishra, E Tadmor Journal of Computational Physics 230 (14), 5587-5609, 2011 | 192 | 2011 |

Sparse tensor multi-level Monte Carlo finite volume methods for hyperbolic conservation laws with random initial data S Mishra, C Schwab Mathematics of computation 81 (280), 1979-2018, 2012 | 181 | 2012 |

Deep learning observables in computational fluid dynamics KO Lye, S Mishra, D Ray Journal of Computational Physics 410, 109339, 2020 | 173 | 2020 |

Well-balanced schemes for the Euler equations with gravitation R Käppeli, S Mishra Journal of Computational Physics 259, 199-219, 2014 | 163 | 2014 |

Multi-level Monte Carlo finite volume methods for nonlinear systems of conservation laws in multi-dimensions S Mishra, C Schwab, J Šukys Journal of Computational Physics 231 (8), 3365-3388, 2012 | 150 | 2012 |

On the approximation of functions by tanh neural networks T De Ryck, S Lanthaler, S Mishra Neural Networks 143, 732-750, 2021 | 139 | 2021 |

Construction of approximate entropy measure-valued solutions for hyperbolic systems of conservation laws US Fjordholm, R Käppeli, S Mishra, E Tadmor Foundations of Computational Mathematics 17, 763-827, 2017 | 136 | 2017 |

A survey on oversmoothing in graph neural networks TK Rusch, MM Bronstein, S Mishra arXiv preprint arXiv:2303.10993, 2023 | 127 | 2023 |

Entropy stable shock capturing space–time discontinuous Galerkin schemes for systems of conservation laws A Hiltebrand, S Mishra Numerische Mathematik 126, 103-151, 2014 | 124 | 2014 |

Tunneling time and weak measurement in strong field ionization T Zimmermann, S Mishra, BR Doran, DF Gordon, AS Landsman Physical review letters 116 (23), 233603, 2016 | 118 | 2016 |

Error estimates for physics-informed neural networks approximating the Navier–Stokes equations T De Ryck, AD Jagtap, S Mishra IMA Journal of Numerical Analysis 44 (1), 83-119, 2024 | 117 | 2024 |

On the computation of measure-valued solutions US Fjordholm, S Mishra, E Tadmor Acta numerica 25, 567-679, 2016 | 107 | 2016 |

Physics informed neural networks for simulating radiative transfer S Mishra, R Molinaro Journal of Quantitative Spectroscopy and Radiative Transfer 270, 107705, 2021 | 103 | 2021 |

Uncertainty quantification in computational fluid dynamics TJ Barth, S Mishra, C Schwab Springer International Publishing, 2013 | 99 | 2013 |