| 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 | 586 | 2020 |
| 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 | 211 | 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 | 143 | 2020 |
| Model reduction and neural networks for parametric PDEs K Bhattacharya, B Hosseini, NB Kovachki, AM Stuart arXiv preprint arXiv:2005.03180, 2020 | 138 | 2020 |
| Neural operator: Learning maps between function spaces N Kovachki, Z Li, B Liu, K Azizzadenesheli, K Bhattacharya, A Stuart, ... arXiv preprint arXiv:2108.08481, 2021 | 124 | 2021 |
| Ensemble Kalman inversion: a derivative-free technique for machine learning tasks NB Kovachki, AM Stuart Inverse Problems 35 (9), 095005, 2019 | 86 | 2019 |
| On universal approximation and error bounds for fourier neural operators N Kovachki, S Lanthaler, S Mishra The Journal of Machine Learning Research 22 (1), 13237-13312, 2021 | 57 | 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 | 51 | 2019 |
| Physics-informed neural operator for learning partial differential equations Z Li, H Zheng, N Kovachki, D Jin, H Chen, B Liu, K Azizzadenesheli, ... arXiv preprint arXiv:2111.03794, 2021 | 48 | 2021 |
| 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 | 27 | 2020 |
| 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 | 20 | 2010 |
| 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 | 19 | 2022 |
| 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, 2021 | 19 | 2021 |
| 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 | 18 | 2022 |
| B. liu, K. Bhattacharya, A. Stuart, and A. Anandkumar, Fourier neural operator for parametric partial differential equations Z Li, NB Kovachki, K Azizzadenesheli International Conference on Learning Representations, 1, 2021 | 18 | 2021 |
| Continuous time analysis of momentum methods NB Kovachki, AM Stuart The Journal of Machine Learning Research 22 (1), 760-799, 2021 | 18 | 2021 |
| Markov neural operators for learning chaotic systems Z Li, N Kovachki, K Azizzadenesheli, B Liu, K Bhattacharya, A Stuart, ... arXiv preprint arXiv:2106.06898, 2021 | 17 | 2021 |
| Conditional sampling with monotone GANs N Kovachki, R Baptista, B Hosseini, Y Marzouk arXiv preprint arXiv:2006.06755, 2020 | 17 | 2020 |
| Convergence rates for learning linear operators from noisy data MV de Hoop, NB Kovachki, NH Nelsen, AM Stuart arXiv preprint arXiv:2108.12515, 2021 | 13 | 2021 |
| Analysis of momentum methods NB Kovachki, AM Stuart arXiv, 2019 | 10 | 2019 |
