| Machine learning–accelerated computational fluid dynamics D Kochkov, JA Smith, A Alieva, Q Wang, MP Brenner, S Hoyer Proceedings of the National Academy of Sciences 118 (21), e2101784118, 2021 | 790 | 2021 |
| Macroscopically Degenerate Exactly Solvable Point in the Spin- Kagome Quantum Antiferromagnet HJ Changlani, D Kochkov, K Kumar, BK Clark, E Fradkin Physical review letters 120 (11), 117202, 2018 | 65 | 2018 |
| Learned discretizations for passive scalar advection in a two-dimensional turbulent flow J Zhuang, D Kochkov, Y Bar-Sinai, MP Brenner, S Hoyer Physical Review Fluids 6 (6), 064605, 2021 | 61 | 2021 |
| Learned coarse models for efficient turbulence simulation K Stachenfeld, DB Fielding, D Kochkov, M Cranmer, T Pfaff, J Godwin, ... arXiv preprint arXiv:2112.15275, 2021 | 56 | 2021 |
| Neural general circulation models D Kochkov, J Yuval, I Langmore, P Norgaard, J Smith, G Mooers, J Lottes, ... arXiv preprint arXiv:2311.07222, 2023 | 34 | 2023 |
| Learned simulators for turbulence K Stachenfeld, DB Fielding, D Kochkov, M Cranmer, T Pfaff, J Godwin, ... International conference on learning representations, 2021 | 34 | 2021 |
| Variational optimization in the AI era: Computational graph states and supervised wave-function optimization D Kochkov, BK Clark arXiv preprint arXiv:1811.12423, 2018 | 33 | 2018 |
| Learning to correct spectral methods for simulating turbulent flows G Dresdner, D Kochkov, P Norgaard, L Zepeda-Núñez, JA Smith, ... arXiv preprint arXiv:2207.00556, 2022 | 32 | 2022 |
| Learning ground states of quantum hamiltonians with graph networks D Kochkov, T Pfaff, A Sanchez-Gonzalez, P Battaglia, BK Clark arXiv preprint arXiv:2110.06390, 2021 | 26 | 2021 |
| Variational data assimilation with a learned inverse observation operator T Frerix, D Kochkov, J Smith, D Cremers, M Brenner, S Hoyer International Conference on Machine Learning, 3449-3458, 2021 | 26 | 2021 |
| Deviation from the dipole-ice model in the spinel spin-ice candidate D Reig-i-Plessis, SV Geldern, AA Aczel, D Kochkov, BK Clark, ... Physical Review B 99 (13), 134438, 2019 | 10 | 2019 |
| Classical phase diagram of the stuffed honeycomb lattice J Sahoo, D Kochkov, BK Clark, R Flint Physical Review B 98 (13), 134419, 2018 | 9 | 2018 |
| Neural general circulation models for weather and climate D Kochkov, J Yuval, I Langmore, P Norgaard, J Smith, G Mooers, ... Nature, 1-7, 2024 | 5 | 2024 |
| Learning general-purpose cnn-based simulators for astrophysical turbulence A Sanchez-Gonzalez, K Stachenfeld, D Fielding, D Kochkov, M Cranmer, ... ICLR 2021 SimDL Workshop, 2021 | 5 | 2021 |
| Disentangled sparsity networks for explainable AI M Cranmer, C Cui, DB Fielding, S Ho, A Sanchez-Gonzalez, ... Workshop on Sparse Neural Networks 7, 2021 | 4 | 2021 |
| Machine learning accelerated computational fluid dynamics A Alieva, D Kochkov, JA Smith, M Brenner, Q Wang, S Hoyer Proceedings of the National Academy of Sciences USA, 2021 | 3 | 2021 |
| Neural General Circulation Models for Weather and Climate S Hoyer, J Yuval, D Kochkov, I Langmore, P Norgaard, G Mooers, ... AGU23, 2023 | 2 | 2023 |
| Learning latent field dynamics of pdes D Kochkov, A Sanchez-Gonzalez, JA Smith, TJ Pfaff, P Battaglia, ... Third Workshop on Machine Learning and the Physical Sciences (NeurIPS 2020), 2020 | 2 | 2020 |
| On numerical methods in quantum spin systems D Kochkov University of Illinois at Urbana-Champaign, 2019 | 2 | 2019 |
| Learning latent field dynamics of PDEs A Sanchez, D Kochkov, JA Smith, M Brenner, P Battaglia, TJ Pfaff Advances in Neural Information Processing Systems, 2020 | 1 | 2020 |
