(orcid 0000-0001-9091-7266)
Liu, Ziming; Stuart, Andrew M. et al. (2022) Second Order Ensemble Langevin Method for Sampling and Inverse Problems arXiv; https://doi.org/10.48550/arXiv.2208.04506
Dunbar, Oliver R. A.; Howland, Michael F. et al. (2022) Ensemble-Based Experimental Design for Targeted High-Resolution Simulations to Inform Climate Models https://doi.org/10.1002/essoar.10510142.1
de Hoop, Maarten V.; Kovachki, Nikola B. et al. (2021) Convergence Rates for Learning Linear Operators from Noisy Data arXiv; https://doi.org/10.48550/arXiv.2108.12515
Kovachki, Nikola; Li, Zongyi et al. (2021) Neural Operator: Learning Maps Between Function Spaces arXiv; https://doi.org/10.48550/arXiv.2108.08481
Levine, Matthew E.; Stuart, Andrew M. (2021) A Framework for Machine Learning of Model Error in Dynamical Systems arXiv; https://doi.org/10.48550/arXiv.2107.06658
Li, Zongyi; Kovachki, Nikola et al. (2021) Learning Dissipative Dynamics in Chaotic Systems arXiv; https://doi.org/10.48550/arXiv.2106.06898
Li, Zongyi; Kovachki, Nikola et al. (2020) Fourier Neural Operator for Parametric Partial Differential Equations arXiv; https://doi.org/10.48550/arXiv.2010.08895
Schneider, Tapio; Stuart, Andrew M. et al. (2020) Ensemble Kalman Inversion for Sparse Learning of Dynamical Systems from Time-Averaged Data arXiv; https://doi.org/10.48550/arXiv.2007.06175
Bhattacharya, Kaushik; Hosseini, Bamdad et al. (2020) Model Reduction and Neural Networks for Parametric PDEs arXiv; https://doi.org/10.48550/arXiv.2005.03180
Schneider, Tapio; Stuart, Andrew M. et al. (2020) Learning Stochastic Closures Using Ensemble Kalman Inversion arXiv; https://doi.org/10.48550/arXiv.2004.08376
Li, Zongyi; Kovachki, Nikola et al. (2020) Neural Operator: Graph Kernel Network for Partial Differential Equations arXiv; https://doi.org/10.48550/arXiv.2003.03485
Albers, D. J.; Levine, M. E. et al. (2019) A Simple Modeling Framework For Prediction In The Human Glucose-Insulin System arXiv; https://doi.org/10.48550/arXiv.1910.14193
Kovachki, Nikola B.; Stuart, Andrew M. (2019) Analysis Of Momentum Methods arXiv; https://doi.org/10.48550/arXiv.1906.04285
Dunlop, Matthew M.; Helin, Tapio et al. (2019) Hyperparameter Estimation in Bayesian MAP Estimation: Parameterizations and Consistency arXiv; https://doi.org/10.48550/arXiv.1905.04365
Chen, Victor; Dunlop, Matthew M. et al. (2018) Dimension-Robust MCMC in Bayesian Inverse Problems arXiv; https://doi.org/10.48550/arXiv.1803.03344
Dunlop, Matthew M.; Elliott, Charles M. et al. (2017) Reconciling Bayesian and Total Variation Methods for Binary Inversion arXiv; https://doi.org/10.48550/arXiv.1706.01960
Schillings, C.; Stuart, A. M. (2017) Convergence Analysis of the Ensemble Kalman Filter for Inverse Problems: the Noisy Case https://resolver.caltech.edu/CaltechAUTHORS:20170612-123329512 (Submitted)
Lu, Yulong; Stuart, Andrew M. et al. (2016) Gaussian approximations for transition paths in molecular dynamics https://doi.org/10.48550/arXiv.1604.06594 (Submitted)
Brett, C. E. A.; Lam, K. F. et al. (2011) Stability of Filters for the Navier-Stokes Equation https://doi.org/10.48550/arXiv.1110.2527 (Submitted)