Ensemble Kalman inversion: a derivative-free technique for machine learning tasks von Kovachki, Nikola B, Stuart, Andrew M

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Regression Clustering for Improved Accuracy and Training Costs with Molecular-Orbital-Based Machine Learning von Cheng, Lixue, Kovachki, Nikola B, Welborn, Matthew, Miller, Thomas F

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An approximation theory framework for measure-transport sampling algorithms von Baptista, Ricardo, Hosseini, Bamdad, Kovachki, Nikola, Marzouk, Youssef, Sagiv, Amir

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Model Reduction And Neural Networks For Parametric PDEs von Bhattacharya, Kaushik, Hosseini, Bamdad, Kovachki, Nikola B., Stuart, Andrew M.

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A learning-based multiscale method and its application to inelastic impact problems von Liu, Burigede, Kovachki, Nikola, Li, Zongyi, Azizzadenesheli, Kamyar, Anandkumar, Anima, Stuart, Andrew M., Bhattacharya, Kaushik

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Neural operators for accelerating scientific simulations and design von Azizzadenesheli, Kamyar, Kovachki, Nikola, Li, Zongyi, Liu-Schiaffini, Miguel, Kossaifi, Jean, Anandkumar, Anima

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Multiscale modeling of materials: Computing, data science, uncertainty and goal-oriented optimization von Kovachki, Nikola, Liu, Burigede, Sun, Xingsheng, Zhou, Hao, Bhattacharya, Kaushik, Ortiz, Michael, Stuart, Andrew

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Learning Homogenization for Elliptic Operators von Bhattacharya, Kaushik, Kovachki, Nikola B., Rajan, Aakila, Stuart, Andrew M., Trautner, Margaret

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Conditional Sampling with Monotone GANs: From Generative Models to Likelihood-Free Inference von Baptista, Ricardo, Hosseini, Bamdad, Kovachki, Nikola B., Marzouk, Youssef M.

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Convergence Rates for Learning Linear Operators from Noisy Data von de Hoop, Maarten V., Kovachki, Nikola B., Nelsen, Nicholas H., Stuart, Andrew M.

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Physics-Informed Neural Operator for Learning Partial Differential Equations von Li, Zongyi, Zheng, Hongkai, Kovachki, Nikola, Jin, David, Chen, Haoxuan, Liu, Burigede, Azizzadenesheli, Kamyar, Anandkumar, Anima

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Data Complexity Estimates for Operator Learning von Kovachki, Nikola B, Lanthaler, Samuel, Mhaskar, Hrushikesh

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Operator Learning: Algorithms and Analysis von Kovachki, Nikola B, Lanthaler, Samuel, Stuart, Andrew M

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Multi-Grid Tensorized Fourier Neural Operator for High-Resolution PDEs von Kossaifi, Jean, Kovachki, Nikola, Azizzadenesheli, Kamyar, Anandkumar, Anima

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On universal approximation and error bounds for Fourier Neural Operators von Kovachki, Nikola, Lanthaler, Samuel, Mishra, Siddhartha

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Warped Diffusion: Solving Video Inverse Problems with Image Diffusion Models von Daras, Giannis, Nie, Weili, Kreis, Karsten, Dimakis, Alex, Mardani, Morteza, Kovachki, Nikola Borislavov, Vahdat, Arash

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Ensemble Kalman Diffusion Guidance: A Derivative-free Method for Inverse Problems von Zheng, Hongkai, Chu, Wenda, Wang, Austin, Kovachki, Nikola, Baptista, Ricardo, Yue, Yisong

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Continuum Attention for Neural Operators von Calvello, Edoardo, Kovachki, Nikola B, Levine, Matthew E, Stuart, Andrew M

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Data Complexity Estimates for Operator Learning von Kovachki, Nikola B, Lanthaler, Samuel, Mhaskar, Hrushikesh

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Neural Operators for Accelerating Scientific Simulations and Design von Azizzadenesheli, Kamyar, Kovachki, Nikola, Li, Zongyi, Liu-Schiaffini, Miguel, Kossaifi, Jean, Anandkumar, Anima

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