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Nikola Kovachki
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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
15842020
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
4842020
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
4822023
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
2992020
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
2842021
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
2392021
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
1962021
Ensemble Kalman inversion: a derivative-free technique for machine learning tasks
NB Kovachki, AM Stuart
Inverse Problems 35 (9), 095005, 2019
1312019
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
882020
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
782021
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
642019
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
492022
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
492010
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
432023
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
412021
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
392022
Continuous time analysis of momentum methods
NB Kovachki, AM Stuart
Journal of Machine Learning Research 22 (17), 1-40, 2021
322021
Conditional sampling with monotone GANs
N Kovachki, R Baptista, B Hosseini, Y Marzouk
arXiv preprint arXiv:2006.06755 2, 12, 2020
272020
Geometry-informed neural operator for large-scale 3d pdes
Z Li, N Kovachki, C Choy, B Li, J Kossaifi, S Otta, MA Nabian, M Stadler, ...
Advances in Neural Information Processing Systems 36, 2024
182024
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
182022
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