| 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 | 334 | 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 | 326 | 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 | 295 | 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 | 290 | 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 | 263 | 2023 |
| A survey on oversmoothing in graph neural networks TK Rusch, MM Bronstein, S Mishra arXiv preprint arXiv:2303.10993, 2023 | 233 | 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 | 215 | 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 | 204 | 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 | 188 | 2012 |
| Deep learning observables in computational fluid dynamics KO Lye, S Mishra, D Ray Journal of Computational Physics 410, 109339, 2020 | 185 | 2020 |
| On the approximation of functions by tanh neural networks T De Ryck, S Lanthaler, S Mishra Neural Networks 143, 732-750, 2021 | 170 | 2021 |
| Well-balanced schemes for the Euler equations with gravitation R Käppeli, S Mishra Journal of Computational Physics 259, 199-219, 2014 | 169 | 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 | 156 | 2012 |
| 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 | 139 | 2024 |
| 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 | 139 | 2017 |
| Graph-coupled oscillator networks TK Rusch, B Chamberlain, J Rowbottom, S Mishra, M Bronstein International Conference on Machine Learning, 18888-18909, 2022 | 135 | 2022 |
| Entropy stable shock capturing space–time discontinuous Galerkin schemes for systems of conservation laws A Hiltebrand, S Mishra Numerische Mathematik 126, 103-151, 2014 | 133 | 2014 |
| Convolutional neural operators for robust and accurate learning of PDEs B Raonic, R Molinaro, T De Ryck, T Rohner, F Bartolucci, R Alaifari, ... Advances in Neural Information Processing Systems 36, 2024 | 126* | 2024 |
| Physics informed neural networks for simulating radiative transfer S Mishra, R Molinaro Journal of Quantitative Spectroscopy and Radiative Transfer 270, 107705, 2021 | 121 | 2021 |
| 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 | 121 | 2016 |
