Stuart, Andrew M.

Monograph from CaltechAUTHORS

• Liu, Ziming and Stuart, Andrew M., et el. (2022) Second Order Ensemble Langevin Method for Sampling and Inverse Problems; 10.48550/arXiv.2208.04506
• de Hoop, Maarten V. and Kovachki, Nikola B., et el. (2022) Convergence Rates for Learning Linear Operators from Noisy Data; 10.48550/arXiv.2108.12515
• Dunbar, Oliver R. A. and Howland, Michael F., et el. (2022) Ensemble-Based Experimental Design for Targeted High-Resolution Simulations to Inform Climate Models; 10.1002/essoar.10510142.1
• Kovachki, Nikola and Li, Zongyi, et el. (2021) Neural Operator: Learning Maps Between Function Spaces; 10.48550/arXiv.2108.08481
• Levine, Matthew E. and Stuart, Andrew M. (2021) A Framework for Machine Learning of Model Error in Dynamical Systems; 10.48550/arXiv.2107.06658
• Li, Zongyi and Kovachki, Nikola, et el. (2021) Learning Dissipative Dynamics in Chaotic Systems; 10.48550/arXiv.2106.06898
• Schneider, Tapio and Stuart, Andrew M., et el. (2020) Learning Stochastic Closures Using Ensemble Kalman Inversion; 10.48550/arXiv.2004.08376
• Albers, D. J. and Levine, M. E., et el. (2020) A Simple Modeling Framework For Prediction In The Human Glucose-Insulin System; 10.48550/arXiv.1910.14193
• Schneider, Tapio and Stuart, Andrew M., et el. (2020) Ensemble Kalman Inversion for Sparse Learning of Dynamical Systems from Time-Averaged Data; 10.48550/arXiv.2007.06175
• Li, Zongyi and Kovachki, Nikola, et el. (2020) Fourier Neural Operator for Parametric Partial Differential Equations; 10.48550/arXiv.2010.08895
• Bhattacharya, Kaushik and Hosseini, Bamdad, et el. (2020) Model Reduction and Neural Networks for Parametric PDEs; 10.48550/arXiv.2005.03180
• Li, Zongyi and Kovachki, Nikola, et el. (2020) Neural Operator: Graph Kernel Network for Partial Differential Equations; 10.48550/arXiv.2003.03485
• Kovachki, Nikola B. and Stuart, Andrew M. (2019) Analysis Of Momentum Methods; 10.48550/arXiv.1906.04285
• Dunlop, Matthew M. and Helin, Tapio, et el. (2019) Hyperparameter Estimation in Bayesian MAP Estimation: Parameterizations and Consistency; 10.48550/arXiv.1905.04365
• Dunlop, Matthew M. and Elliott, Charles M., et el. (2019) Reconciling Bayesian and Total Variation Methods for Binary Inversion; 10.48550/arXiv.1706.01960
• Chen, Victor and Dunlop, Matthew M., et el. (2019) Dimension-Robust MCMC in Bayesian Inverse Problems; 10.48550/arXiv.1803.03344
• Brett, C. E. A. and Lam, K. F., et el. (2017) Stability of Filters for the Navier-Stokes Equation; 10.48550/arXiv.1110.2527
• Schillings, C. and Stuart, A. M. (2017) Convergence Analysis of the Ensemble Kalman Filter for Inverse Problems: the Noisy Case
• Lu, Yulong and Stuart, Andrew M., et el. (2016) Gaussian approximations for transition paths in molecular dynamics; 10.48550/arXiv.1604.06594