Stuart, Andrew

8 publications

ICLR 2025 Gradient-Free Generation for Hard-Constrained Systems Chaoran Cheng, Boran Han, Danielle C. Maddix, Abdul Fatir Ansari, Andrew Stuart, Michael W. Mahoney, Bernie Wang

ICLRW 2024 Comparing and Contrasting Deep Learning Weather Prediction Backbones on Navier-Stokes Dynamics Matthias Karlbauer, Danielle C. Maddix, Abdul Fatir Ansari, Boran Han, Gaurav Gupta, Bernie Wang, Andrew Stuart, Michael W. Mahoney

ICML 2024 Using Uncertainty Quantification to Characterize and Improve Out-of-Domain Learning for PDEs S Chandra Mouli, Danielle C. Maddix, Shima Alizadeh, Gaurav Gupta, Andrew Stuart, Michael W. Mahoney, Bernie Wang

JMLR 2023 Neural Operator: Learning Maps Between Function Spaces with Applications to PDEs Nikola Kovachki, Zongyi Li, Burigede Liu, Kamyar Azizzadenesheli, Kaushik Bhattacharya, Andrew Stuart, Anima Anandkumar

NeurIPS 2022 Learning Chaotic Dynamics in Dissipative Systems Zongyi Li, Miguel Liu-Schiaffini, Nikola Kovachki, Kamyar Azizzadenesheli, Burigede Liu, Kaushik Bhattacharya, Andrew Stuart, Anima Anandkumar

ICLR 2021 Fourier Neural Operator for Parametric Partial Differential Equations Zongyi Li, Nikola Borislavov Kovachki, Kamyar Azizzadenesheli, Burigede Liu, Kaushik Bhattacharya, Andrew Stuart, Anima Anandkumar

NeurIPS 2020 Multipole Graph Neural Operator for Parametric Partial Differential Equations Zongyi Li, Nikola Kovachki, Kamyar Azizzadenesheli, Burigede Liu, Andrew Stuart, Kaushik Bhattacharya, Anima Anandkumar

ICLRW 2020 Neural Operator: Graph Kernel Network for Partial Differential Equations Anima Anandkumar, Kamyar Azizzadenesheli, Kaushik Bhattacharya, Nikola Kovachki, Zongyi Li, Burigede Liu, Andrew Stuart