Nikola B. Kovachki

46 papers A* 4Journal 42

Year	Rank	Type	Title / Venue / Authors
2026	J	`jnl`	Demystifying Data-Driven Probabilistic Medium-Range Weather Forecasting. CoRR Jean Kossaifi, Nikola B. Kovachki, Morteza Mardani, Daniel V. Leibovici, Suman Ravuri, Ira Shokar, Edoardo Calvello, Mohammad Shoaib Abbas, Peter Harrington, Ashay Subramaniam, Noah D. Brenowitz, Boris Bonev, Wonmin Byeon, Karsten Kreis, Dale R. Durran, Arash Vahdat, Mike Pritchard, Jan Kautz
2026	J	`jnl`	Operator Learning for Smoothing and Forecasting. CoRR Edoardo Calvello, Elizabeth Carlson, Nikola B. Kovachki, Michael N. Manta, Andrew M. Stuart
2025	J	`jnl`	A Learning-based Domain Decomposition Method. CoRR Rui Wu, Nikola B. Kovachki, Burigede Liu
2025	J	`jnl`	Continuum Attention for Neural Operators. J. Mach. Learn. Res. Edoardo Calvello, Nikola B. Kovachki, Matthew E. Levine, Andrew M. Stuart
2025	J	`jnl`	Derivative-Informed Fourier Neural Operator: Universal Approximation and Applications to PDE-Constrained Optimization. CoRR Boyuan Yao, Dingcheng Luo, Lianghao Cao, Nikola B. Kovachki, Thomas O'Leary-Roseberry, Omar Ghattas
2025	J	`jnl`	Extension and neural operator approximation of the electrical impedance tomography inverse map. CoRR Maarten V. de Hoop, Nikola B. Kovachki, Matti Lassas, Nicholas H. Nelsen
2025	J	`jnl`	Geometric Operator Learning with Optimal Transport. CoRR Xinyi Li, Zongyi Li, Nikola B. Kovachki, Anima Anandkumar
2025	J	`jnl`	Geometric Operator Learning with Optimal Transport. J. Mach. Learn. Res. Xinyi Li, Zongyi Li, Nikola B. Kovachki, Anima Anandkumar
2025	J	`jnl`	InverseBench: Benchmarking Plug-and-Play Diffusion Priors for Inverse Problems in Physical Sciences. CoRR Hongkai Zheng, Wenda Chu, Bingliang Zhang, Zihui Wu, Austin Wang, Berthy T. Feng, Caifeng Zou, Yu Sun, Nikola B. Kovachki, Zachary E. Ross, Katherine L. Bouman, Yisong Yue
2025	J	`jnl`	Memorization and Regularization in Generative Diffusion Models. CoRR Ricardo Baptista, Agnimitra Dasgupta, Nikola B. Kovachki, Assad A. Oberai, Andrew M. Stuart
2025	J	`jnl`	Score-Based Diffusion Models in Function Space. J. Mach. Learn. Res. 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
2024	J	`jnl`	A Library for Learning Neural Operators. CoRR Jean Kossaifi, Nikola B. Kovachki, Zongyi Li, David Pitt, Miguel Liu-Schiaffini, Robert Joseph George, Boris Bonev, Kamyar Azizzadenesheli, Julius Berner, Anima Anandkumar
2024	J	`jnl`	An approximation theory framework for measure-transport sampling algorithms. Math. Comput. Ricardo Baptista, Bamdad Hosseini, Nikola B. Kovachki, Youssef Marzouk, Amir Sagiv
2024	J	`jnl`	Conditional Sampling with Monotone GANs: From Generative Models to Likelihood-Free Inference. SIAM/ASA J. Uncertain. Quantification Ricardo Baptista, Bamdad Hosseini, Nikola B. Kovachki, Youssef M. Marzouk
2024	J	`jnl`	Continuum Attention for Neural Operators. CoRR Edoardo Calvello, Nikola B. Kovachki, Matthew E. Levine, Andrew M. Stuart
2024	J	`jnl`	Data Complexity Estimates for Operator Learning. CoRR Nikola B. Kovachki, Samuel Lanthaler, Hrushikesh Mhaskar
2024	J	`jnl`	Ensemble Kalman Diffusion Guidance: A Derivative-free Method for Inverse Problems. CoRR Hongkai Zheng, Wenda Chu, Austin Wang, Nikola B. Kovachki, Ricardo Baptista, Yisong Yue
2024	J	`jnl`	Learning Homogenization for Elliptic Operators. SIAM J. Numer. Anal. Kaushik Bhattacharya, Nikola B. Kovachki, Aakila Rajan, Andrew M. Stuart, Margaret Trautner
2024	J	`jnl`	Operator Learning: Algorithms and Analysis. CoRR Nikola B. Kovachki, Samuel Lanthaler, Andrew M. Stuart
2024	A*	`conf`	Warped Diffusion: Solving Video Inverse Problems with Image Diffusion Models. NeurIPS Giannis Daras, Weili Nie, Karsten Kreis, Alex Dimakis, Morteza Mardani, Nikola B. Kovachki, Arash Vahdat
2023	J	`jnl`	An Approximation Theory Framework for Measure-Transport Sampling Algorithms. CoRR Ricardo Baptista, Bamdad Hosseini, Nikola B. Kovachki, Youssef M. Marzouk, Amir Sagiv
2023	J	`jnl`	Convergence Rates for Learning Linear Operators from Noisy Data. SIAM/ASA J. Uncertain. Quantification Maarten V. de Hoop, Nikola B. Kovachki, Nicholas H. Nelsen, Andrew M. Stuart
2023	A*	`conf`	Geometry-Informed Neural Operator for Large-Scale 3D PDEs. NeurIPS Zongyi Li, Nikola B. Kovachki, Christopher B. Choy, Boyi Li, Jean Kossaifi, Shourya Prakash Otta, Mohammad Amin Nabian, Maximilian Stadler, Christian Hundt, Kamyar Azizzadenesheli, Animashree Anandkumar
2023	J	`jnl`	Learning Homogenization for Elliptic Operators. CoRR Kaushik Bhattacharya, Nikola B. Kovachki, Aakila Rajan, Andrew M. Stuart, Margaret Trautner
2023	J	`jnl`	Multi-Grid Tensorized Fourier Neural Operator for High-Resolution PDEs. CoRR Jean Kossaifi, Nikola B. Kovachki, Kamyar Azizzadenesheli, Anima Anandkumar
2023	J	`jnl`	Neural Operator: Learning Maps Between Function Spaces With Applications to PDEs. J. Mach. Learn. Res. Nikola B. Kovachki, Zongyi Li, Burigede Liu, Kamyar Azizzadenesheli, Kaushik Bhattacharya, Andrew M. Stuart, Anima Anandkumar
2023	J	`jnl`	Neural Operators for Accelerating Scientific Simulations and Design. CoRR Kamyar Azizzadenesheli, Nikola B. Kovachki, Zongyi Li, Miguel Liu-Schiaffini, Jean Kossaifi, Anima Anandkumar
2023	J	`jnl`	Score-based Diffusion Models in Function Space. CoRR 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
2023	J	`jnl`	Tipping Point Forecasting in Non-Stationary Dynamics on Function Spaces. CoRR Miguel Liu-Schiaffini, Clare E. Singer, Nikola B. Kovachki, Tapio Schneider, Kamyar Azizzadenesheli, Anima Anandkumar
2022	A*	`conf`	Learning Chaotic Dynamics in Dissipative Systems. NeurIPS Zongyi Li, Miguel Liu-Schiaffini, Nikola B. Kovachki, Kamyar Azizzadenesheli, Burigede Liu, Kaushik Bhattacharya, Andrew M. Stuart, Anima Anandkumar
2021	J	`jnl`	Continuous Time Analysis of Momentum Methods. J. Mach. Learn. Res. Nikola B. Kovachki, Andrew M. Stuart
2021	J	`jnl`	Convergence Rates for Learning Linear Operators from Noisy Data. CoRR Maarten V. de Hoop, Nikola B. Kovachki, Nicholas H. Nelsen, Andrew M. Stuart
2021	J	`jnl`	Markov Neural Operators for Learning Chaotic Systems. CoRR Zongyi Li, Nikola B. Kovachki, Kamyar Azizzadenesheli, Burigede Liu, Kaushik Bhattacharya, Andrew M. Stuart, Anima Anandkumar
2021	J	`jnl`	Neural Operator: Learning Maps Between Function Spaces. CoRR Nikola B. Kovachki, Zongyi Li, Burigede Liu, Kamyar Azizzadenesheli, Kaushik Bhattacharya, Andrew M. Stuart, Anima Anandkumar
2021	J	`jnl`	On Universal Approximation and Error Bounds for Fourier Neural Operators. J. Mach. Learn. Res. Nikola B. Kovachki, Samuel Lanthaler, Siddhartha Mishra
2021	J	`jnl`	On universal approximation and error bounds for Fourier Neural Operators. CoRR Nikola B. Kovachki, Samuel Lanthaler, Siddhartha Mishra
2021	J	`jnl`	Physics-Informed Neural Operator for Learning Partial Differential Equations. CoRR Zongyi Li, Hongkai Zheng, Nikola B. Kovachki, David Jin, Haoxuan Chen, Burigede Liu, Kamyar Azizzadenesheli, Anima Anandkumar
2020	J	`jnl`	Conditional Sampling With Monotone GANs. CoRR Nikola B. Kovachki, Ricardo Baptista, Bamdad Hosseini, Youssef M. Marzouk
2020	J	`jnl`	Fourier Neural Operator for Parametric Partial Differential Equations. CoRR Zongyi Li, Nikola B. Kovachki, Kamyar Azizzadenesheli, Burigede Liu, Kaushik Bhattacharya, Andrew M. Stuart, Anima Anandkumar
2020	J	`jnl`	Model Reduction and Neural Networks for Parametric PDEs. CoRR Kaushik Bhattacharya, Bamdad Hosseini, Nikola B. Kovachki, Andrew M. Stuart
2020	A*	`conf`	Multipole Graph Neural Operator for Parametric Partial Differential Equations. NeurIPS Zongyi Li, Nikola B. Kovachki, Kamyar Azizzadenesheli, Burigede Liu, Andrew M. Stuart, Kaushik Bhattacharya, Anima Anandkumar
2020	J	`jnl`	Multipole Graph Neural Operator for Parametric Partial Differential Equations. CoRR Zongyi Li, Nikola B. Kovachki, Kamyar Azizzadenesheli, Burigede Liu, Kaushik Bhattacharya, Andrew M. Stuart, Anima Anandkumar
2020	J	`jnl`	Neural Operator: Graph Kernel Network for Partial Differential Equations. CoRR Zongyi Li, Nikola B. Kovachki, Kamyar Azizzadenesheli, Burigede Liu, Kaushik Bhattacharya, Andrew M. Stuart, Anima Anandkumar
2019	J	`jnl`	Analysis Of Momentum Methods. CoRR Nikola B. Kovachki, Andrew M. Stuart
2019	J	`jnl`	Regression-clustering for Improved Accuracy and Training Cost with Molecular-Orbital-Based Machine Learning. CoRR Lixue Cheng, Nikola B. Kovachki, Matthew Welborn, Thomas F. Miller III
2018	J	`jnl`	Ensemble Kalman Inversion: A Derivative-Free Technique For Machine Learning Tasks. CoRR Nikola B. Kovachki, Andrew M. Stuart