Dmitrii Kochkov
Cited by
Cited by
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
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
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
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
Neural general circulation models
D Kochkov, J Yuval, I Langmore, P Norgaard, J Smith, G Mooers, J Lottes, ...
arXiv preprint arXiv:2311.07222, 2023
Learned simulators for turbulence
K Stachenfeld, DB Fielding, D Kochkov, M Cranmer, T Pfaff, J Godwin, ...
International conference on learning representations, 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
Learning to correct spectral methods for simulating turbulent flows
G Dresdner, D Kochkov, P Norgaard, L Zepeda-Nez, JA Smith, ...
arXiv preprint arXiv:2207.00556, 2022
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
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
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
Classical phase diagram of the stuffed honeycomb lattice
J Sahoo, D Kochkov, BK Clark, R Flint
Physical Review B 98 (13), 134419, 2018
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
Disentangled sparsity networks for explainable AI
M Cranmer, C Cui, DB Fielding, S Ho, A Sanchez-Gonzalez, ...
Workshop on Sparse Neural Networks 7, 2021
Neural General Circulation Models for Weather and Climate
S Hoyer, J Yuval, D Kochkov, I Langmore, P Norgaard, G Mooers, ...
AGU23, 2023
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
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
On numerical methods in quantum spin systems
D Kochkov
University of Illinois at Urbana-Champaign, 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
Variational optimization in the AI era: supervised wave-function optimization and computational graph states.
D Kochkov, B Clark
APS March Meeting Abstracts 2019, H18. 007, 2019
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