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ORCIDNikola B. Kovachki
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2020 – today
- 2023
[i14]Jae Hyun Lim, Nikola B. Kovachki, Ricardo Baptista, Christopher Beckham, Kamyar Azizzadenesheli, Jean Kossaifi, Vikram Voleti, Jiaming Song, Karsten Kreis, Jan Kautz, Christopher Pal, Arash Vahdat, Anima Anandkumar:
Score-based Diffusion Models in Function Space. CoRR abs/2302.07400 (2023)- [i13]Ricardo Baptista, Bamdad Hosseini, Nikola B. Kovachki, Youssef M. Marzouk, Amir Sagiv:
An Approximation Theory Framework for Measure-Transport Sampling Algorithms. CoRR abs/2302.13965 (2023) - 2022
- [c3]Zongyi Li, Miguel Liu-Schiaffini, Nikola B. Kovachki, Kamyar Azizzadenesheli, Burigede Liu, Kaushik Bhattacharya, Andrew M. Stuart, Anima Anandkumar:
Learning Chaotic Dynamics in Dissipative Systems. NeurIPS 2022 - [d1]Zongyi Li
, Miguel Liu-Schiaffini, Nikola Borislavov Kovachki, Burigede Liu, Kamyar Azizzadenesheli, Kaushik Bhattacharya, Andrew M. Stuart, Anima Anandkumar:
Learning Dissipative Dynamics in Chaotic Systems (Datasets). Zenodo, 2022
- 2021
- [j1]Nikola B. Kovachki, Andrew M. Stuart:
Continuous Time Analysis of Momentum Methods. J. Mach. Learn. Res. 22: 17:1-17:40 (2021) - [c2]Zongyi Li, Nikola Borislavov Kovachki, Kamyar Azizzadenesheli, Burigede Liu, Kaushik Bhattacharya, Andrew M. Stuart, Anima Anandkumar:
Fourier Neural Operator for Parametric Partial Differential Equations. ICLR 2021 - [i12]Zongyi Li, Nikola B. Kovachki, Kamyar Azizzadenesheli, Burigede Liu, Kaushik Bhattacharya, Andrew M. Stuart, Anima Anandkumar:
Markov Neural Operators for Learning Chaotic Systems. CoRR abs/2106.06898 (2021) - [i11]Nikola B. Kovachki, Zongyi Li, Burigede Liu, Kamyar Azizzadenesheli, Kaushik Bhattacharya, Andrew M. Stuart, Anima Anandkumar:
Neural Operator: Learning Maps Between Function Spaces. CoRR abs/2108.08481 (2021) - [i10]Maarten V. de Hoop, Nikola B. Kovachki, Nicholas H. Nelsen, Andrew M. Stuart:
Convergence Rates for Learning Linear Operators from Noisy Data. CoRR abs/2108.12515 (2021) - [i9]Zongyi Li, Hongkai Zheng, Nikola B. Kovachki, David Jin, Haoxuan Chen, Burigede Liu, Kamyar Azizzadenesheli, Anima Anandkumar:
Physics-Informed Neural Operator for Learning Partial Differential Equations. CoRR abs/2111.03794 (2021) - 2020
- [c1]Zongyi Li, Nikola B. Kovachki, Kamyar Azizzadenesheli, Burigede Liu, Andrew M. Stuart, Kaushik Bhattacharya, Anima Anandkumar:
Multipole Graph Neural Operator for Parametric Partial Differential Equations. NeurIPS 2020 - [i8]Zongyi Li, Nikola B. Kovachki, Kamyar Azizzadenesheli, Burigede Liu, Kaushik Bhattacharya, Andrew M. Stuart, Anima Anandkumar:
Neural Operator: Graph Kernel Network for Partial Differential Equations. CoRR abs/2003.03485 (2020) - [i7]Kaushik Bhattacharya, Bamdad Hosseini, Nikola B. Kovachki, Andrew M. Stuart:
Model Reduction and Neural Networks for Parametric PDEs. CoRR abs/2005.03180 (2020) - [i6]Nikola B. Kovachki, Ricardo Baptista, Bamdad Hosseini, Youssef M. Marzouk:
Conditional Sampling With Monotone GANs. CoRR abs/2006.06755 (2020) - [i5]Zongyi Li, Nikola B. Kovachki, Kamyar Azizzadenesheli, Burigede Liu, Kaushik Bhattacharya, Andrew M. Stuart, Anima Anandkumar:
Multipole Graph Neural Operator for Parametric Partial Differential Equations. CoRR abs/2006.09535 (2020) - [i4]Zongyi Li, Nikola B. Kovachki, Kamyar Azizzadenesheli, Burigede Liu, Kaushik Bhattacharya, Andrew M. Stuart, Anima Anandkumar:
Fourier Neural Operator for Parametric Partial Differential Equations. CoRR abs/2010.08895 (2020)
2010 – 2019
- 2019
- [i3]Nikola B. Kovachki, Andrew M. Stuart:
Analysis Of Momentum Methods. CoRR abs/1906.04285 (2019) - [i2]Lixue Cheng, Nikola B. Kovachki, Matthew Welborn, Thomas F. Miller III:
Regression-clustering for Improved Accuracy and Training Cost with Molecular-Orbital-Based Machine Learning. CoRR abs/1909.02041 (2019) - 2018
- [i1]Nikola B. Kovachki, Andrew M. Stuart:
Ensemble Kalman Inversion: A Derivative-Free Technique For Machine Learning Tasks. CoRR abs/1808.03620 (2018)
Coauthor Index
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last updated on 2023-05-13 03:55 CEST by the dblp team
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