| Fourier neural operator for parametric partial differential equations Z Li, N Kovachki, K Azizzadenesheli, B Liu, K Bhattacharya, A Stuart, ... arXiv preprint arXiv:2010.08895, 2020 | 1628 | 2020 |
| Neural operator: Learning maps between function spaces with applications to pdes N Kovachki, Z Li, B Liu, K Azizzadenesheli, K Bhattacharya, A Stuart, ... Journal of Machine Learning Research 24 (89), 1-97, 2023 | 500 | 2023 |
| Neural operator: Graph kernel network for partial differential equations Z Li, N Kovachki, K Azizzadenesheli, B Liu, K Bhattacharya, A Stuart, ... arXiv preprint arXiv:2003.03485, 2020 | 490 | 2020 |
| Multipole graph neural operator for parametric partial differential equations Z Li, N Kovachki, K Azizzadenesheli, B Liu, A Stuart, K Bhattacharya, ... Advances in Neural Information Processing Systems 33, 6755-6766, 2020 | 309 | 2020 |
| Model reduction and neural networks for parametric PDEs K Bhattacharya, B Hosseini, NB Kovachki, AM Stuart The SMAI journal of computational mathematics 7, 121-157, 2021 | 289 | 2021 |
| Physics-informed neural operator for learning partial differential equations Z Li, H Zheng, N Kovachki, D Jin, H Chen, B Liu, K Azizzadenesheli, ... ACM/JMS Journal of Data Science, 2021 | 249 | 2021 |
| 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 | 201 | 2021 |
| Ensemble Kalman inversion: a derivative-free technique for machine learning tasks NB Kovachki, AM Stuart Inverse Problems 35 (9), 095005, 2019 | 132 | 2019 |
| Neural operator: Graph kernel network for partial differential equations A Anandkumar, K Azizzadenesheli, K Bhattacharya, N Kovachki, Z Li, ... ICLR 2020 Workshop on Integration of Deep Neural Models and Differential …, 2020 | 90 | 2020 |
| Burigede liu, Kaushik Bhattacharya, Andrew Stuart, and Anima Anandkumar. Fourier neural operator for parametric partial differential equations Z Li, NB Kovachki, K Azizzadenesheli International Conference on Learning Representations 2 (3), 4, 2021 | 80 | 2021 |
| Regression clustering for improved accuracy and training costs with molecular-orbital-based machine learning L Cheng, NB Kovachki, M Welborn, TF Miller III Journal of chemical theory and computation 15 (12), 6668-6677, 2019 | 64 | 2019 |
| Fourier neural operator for parametric partial differential equations (2020) Z Li, N Kovachki, K Azizzadenesheli, B Liu, K Bhattacharya, A Stuart, ... arXiv preprint arXiv:2010.08895, 2010 | 51 | 2010 |
| A learning-based multiscale method and its application to inelastic impact problems B Liu, N Kovachki, Z Li, K Azizzadenesheli, A Anandkumar, AM Stuart, ... Journal of the Mechanics and Physics of Solids 158, 104668, 2022 | 49 | 2022 |
| Convergence rates for learning linear operators from noisy data MV de Hoop, NB Kovachki, NH Nelsen, AM Stuart SIAM/ASA Journal on Uncertainty Quantification 11 (2), 480-513, 2023 | 44 | 2023 |
| Markov neural operators for learning chaotic systems Z Li, N Kovachki, K Azizzadenesheli, B Liu, K Bhattacharya, A Stuart, ... arXiv preprint arXiv:2106.06898, 25, 2021 | 42 | 2021 |
| Multiscale modeling of materials: Computing, data science, uncertainty and goal-oriented optimization N Kovachki, B Liu, X Sun, H Zhou, K Bhattacharya, M Ortiz, A Stuart Mechanics of Materials 165, 104156, 2022 | 39 | 2022 |
| Continuous time analysis of momentum methods NB Kovachki, AM Stuart Journal of Machine Learning Research 22 (17), 1-40, 2021 | 31 | 2021 |
| Conditional sampling with monotone GANs N Kovachki, R Baptista, B Hosseini, Y Marzouk arXiv preprint arXiv:2006.06755 2, 12, 2020 | 27 | 2020 |
| Score-based diffusion models in function space JH Lim, NB Kovachki, R Baptista, C Beckham, K Azizzadenesheli, ... arXiv preprint arXiv:2302.07400, 2023 | 19 | 2023 |
| Learning chaotic dynamics in dissipative systems Z Li, M Liu-Schiaffini, N Kovachki, K Azizzadenesheli, B Liu, ... Advances in Neural Information Processing Systems 35, 16768-16781, 2022 | 18 | 2022 |
