Andrew M. Stuart

142 papers A* 3Misc 4Journal 134Unranked 1

Year	Rank	Type	Title / Venue / Authors
2026	J	`jnl`	Accuracy of the Ensemble Kalman Filter in the Near-Linear Setting. SIAM J. Numer. Anal. Edoardo Calvello, Pierre Monmarché, Andrew M. Stuart, Urbain Vaes
2026	J	`jnl`	Large Language Models: A Mathematical Formulation. CoRR Ricardo Baptista, Andrew M. Stuart, Son Tran
2026	J	`jnl`	Learning enhanced ensemble filters. J. Comput. Phys. Eviatar Bach, Ricardo Baptista, Edoardo Calvello, Bohan Chen, Andrew M. Stuart
2026	J	`jnl`	Neural ensemble Kalman filter: Data assimilation for compressible flows with shocks. CoRR Xu-Hui Zhou, Lorenzo Beronilla, Michael K. Sleeman, Hangchuan Hu, Matthias Morzfeld, Andrew M. Stuart, Tamer A. Zaki
2026	J	`jnl`	Operator Learning for Smoothing and Forecasting. CoRR Edoardo Calvello, Elizabeth Carlson, Nikola B. Kovachki, Michael N. Manta, Andrew M. Stuart
2026	J	`jnl`	Optimal Experimental Design for Reliable Learning of History-Dependent Constitutive Laws. CoRR Kaushik Bhattacharya, Lianghao Cao, Andrew M. Stuart
2025	J	`jnl`	A Mathematical Perspective On Contrastive Learning. CoRR Ricardo Baptista, Andrew M. Stuart, Son Tran
2025	J	`jnl`	Autoencoders in Function Space. J. Mach. Learn. Res. Justin Bunker, Mark Girolami, Hefin Lambley, Andrew M. Stuart, Timothy John Sullivan
2025	J	`jnl`	Bilevel optimization for learning hyperparameters: Application to solving PDEs and inverse problems with Gaussian processes. CoRR Nicholas H. Nelsen, Houman Owhadi, Andrew M. Stuart, Xianjin Yang, Zongren Zou
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`	Efficient Deconvolution in Populational Inverse Problems. CoRR Arnaud Vadeboncoeur, Mark Girolami, Andrew M. Stuart
2025	J	`jnl`	Efficient Prior Calibration from Indirect Data. SIAM J. Sci. Comput. Ömer Deniz Akyildiz, Mark Girolami, Andrew M. Stuart, Arnaud Vadeboncoeur
2025	J	`jnl`	Ensemble Kalman methods: A mean-field perspective. Acta Numer. Edoardo Calvello, Sebastian Reich, Andrew M. Stuart
2025	J	`jnl`	Error analysis of kernel/GP methods for nonlinear and parametric PDEs. J. Comput. Phys. Pau Batlle, Yifan Chen, Bamdad Hosseini, Houman Owhadi, Andrew M. Stuart
2025	J	`jnl`	Gaussian measures conditioned on nonlinear observations: consistency, MAP estimators, and simulation. Stat. Comput. Yifan Chen, Bamdad Hosseini, Houman Owhadi, Andrew M. Stuart
2025	J	`jnl`	Learning Enhanced Ensemble Filters. CoRR Eviatar Bach, Ricardo Baptista, Edoardo Calvello, Bohan Chen, Andrew M. Stuart
2025	J	`jnl`	Learning Memory and Material Dependent Constitutive Laws. CoRR Kaushik Bhattacharya, Lianghao Cao, George Stepaniants, Andrew M. Stuart, Margaret Trautner
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`	Solving Roughly Forced Nonlinear PDEs via Misspecified Kernel Methods and Neural Networks. CoRR Ricardo Baptista, Edoardo Calvello, Matthieu Darcy, Houman Owhadi, Andrew M. Stuart, Xianjin Yang
2024	J	`jnl`	Accuracy of the Ensemble Kalman Filter in the Near-Linear Setting. CoRR Edoardo Calvello, Pierre Monmarché, Andrew M. Stuart, Urbain Vaes
2024	J	`jnl`	Autoencoders in Function Space. CoRR Justin Bunker, Mark Girolami, Hefin Lambley, Andrew M. Stuart, Tim Sullivan
2024	J	`jnl`	Continuum Attention for Neural Operators. CoRR Edoardo Calvello, Nikola B. Kovachki, Matthew E. Levine, Andrew M. Stuart
2024	J	`jnl`	Discretization Error of Fourier Neural Operators. CoRR Samuel Lanthaler, Andrew M. Stuart, Margaret Trautner
2024	J	`jnl`	Efficient Prior Calibration From Indirect Data. CoRR Ömer Deniz Akyildiz, Mark Girolami, Andrew M. Stuart, Arnaud Vadeboncoeur
2024	J	`jnl`	Efficient, Multimodal, and Derivative-Free Bayesian Inference With Fisher-Rao Gradient Flows. CoRR Yifan Chen, Daniel Zhengyu Huang, Jiaoyang Huang, Sebastian Reich, Andrew M. Stuart
2024	J	`jnl`	Gaussian Measures Conditioned on Nonlinear Observations: Consistency, MAP Estimators, and Simulation. CoRR Yifan Chen, Bamdad Hosseini, Houman Owhadi, Andrew M. Stuart
2024	J	`jnl`	Inverse Problems and Data Assimilation: A Machine Learning Approach. CoRR Eviatar Bach, Ricardo Baptista, Daniel Sanz-Alonso, Andrew M. Stuart
2024	J	`jnl`	Learning About Structural Errors in Models of Complex Dynamical Systems. CoRR Jinlong Wu, Matthew E. Levine, Tapio Schneider, Andrew M. Stuart
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`	Learning Optimal Filters Using Variational Inference. CoRR Enoch Luk, Eviatar Bach, Ricardo Baptista, Andrew M. Stuart
2024	J	`jnl`	Learning about structural errors in models of complex dynamical systems. J. Comput. Phys. Jinlong Wu, Matthew E. Levine, Tapio Schneider, Andrew M. Stuart
2024	J	`jnl`	Operator Learning Using Random Features: A Tool for Scientific Computing. CoRR Nicholas H. Nelsen, Andrew M. Stuart
2024	J	`jnl`	Operator Learning Using Random Features: A Tool for Scientific Computing. SIAM Rev. Nicholas H. Nelsen, Andrew M. Stuart
2024	J	`jnl`	Operator Learning: Algorithms and Analysis. CoRR Nikola B. Kovachki, Samuel Lanthaler, Andrew M. Stuart
2024	J	`jnl`	Statistical Accuracy of Approximate Filtering Methods. CoRR J. A. Carrillo, F. Hoffmann, Andrew M. Stuart, U. Vaes
2024	J	`jnl`	The Mean-Field Ensemble Kalman Filter: Near-Gaussian Setting. SIAM J. Numer. Anal. José A. Carrillo, Franca Hoffmann, Andrew M. Stuart, Urbain Vaes
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	J	`jnl`	Drift Estimation of Multiscale Diffusions Based on Filtered Data. Found. Comput. Math. Assyr Abdulle, Giacomo Garegnani, Grigorios A. Pavliotis, Andrew M. Stuart, Andrea Zanoni
2023	J	`jnl`	Error Analysis of Kernel/GP Methods for Nonlinear and Parametric PDEs. CoRR Pau Batlle, Yifan Chen, Bamdad Hosseini, Houman Owhadi, Andrew M. Stuart
2023	J	`jnl`	Gradient Flows for Sampling: Mean-Field Models, Gaussian Approximations and Affine Invariance. CoRR Yifan Chen, Daniel Zhengyu Huang, Jiaoyang Huang, Sebastian Reich, Andrew M. Stuart
2023	J	`jnl`	Introduction To Gaussian Process Regression In Bayesian Inverse Problems, With New ResultsOn Experimental Design For Weighted Error Measures. CoRR Tapio Helin, Andrew M. Stuart, Aretha L. Teckentrup, Konstantinos Zygalakis
2023	J	`jnl`	Learning Homogenization for Elliptic Operators. CoRR Kaushik Bhattacharya, Nikola B. Kovachki, Aakila Rajan, Andrew M. Stuart, Margaret Trautner
2023	J	`jnl`	Learning Markovian Homogenized Models in Viscoelasticity. Multiscale Model. Simul. Kaushik Bhattacharya, Burigede Liu, Andrew M. Stuart, Margaret Trautner
2023	J	`jnl`	Modeling groundwater levels in California's Central Valley by hierarchical Gaussian process and neural network regression. CoRR Anshuman Pradhan, Kyra H. Adams, Venkat Chandrasekaran, Zhen Liu, John T. Reager, Andrew M. Stuart, Michael J. Turmon
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`	Sampling via Gradient Flows in the Space of Probability Measures. CoRR Yifan Chen, Daniel Zhengyu Huang, Jiaoyang Huang, Sebastian Reich, Andrew M. Stuart
2023	J	`jnl`	The Nonlocal Neural Operator: Universal Approximation. CoRR Samuel Lanthaler, Zongyi Li, Andrew M. Stuart
2023	J	`jnl`	The curse of dimensionality in operator learning. CoRR Samuel Lanthaler, Andrew M. Stuart
2022	J	`jnl`	Derivative-Free Bayesian Inversion Using Multiscale Dynamics. SIAM J. Appl. Dyn. Syst. Grigorios A. Pavliotis, Andrew M. Stuart, Urbain Vaes
2022	J	`jnl`	Efficient Derivative-free Bayesian Inference for Large-Scale Inverse Problems. CoRR Daniel Zhengyu Huang, Jiaoyang Huang, Sebastian Reich, Andrew M. Stuart
2022	J	`jnl`	Ensemble Inference Methods for Models With Noisy and Expensive Likelihoods. SIAM J. Appl. Dyn. Syst. Oliver R. A. Dunbar, Andrew B. Duncan, Andrew M. Stuart, Marie-Therese Wolfram
2022	J	`jnl`	Ensemble Kalman Methods: A Mean Field Perspective. CoRR Edoardo Calvello, Sebastian Reich, Andrew M. Stuart
2022	J	`jnl`	Ensemble Kalman inversion for sparse learning of dynamical systems from time-averaged data. J. Comput. Phys. Tapio Schneider, Andrew M. Stuart, Jinlong Wu
2022	J	`jnl`	Iterated Kalman methodology for inverse problems. J. Comput. Phys. Daniel Zhengyu Huang, Tapio Schneider, Andrew M. Stuart
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
2022	J	`jnl`	Learning Markovian Homogenized Models in Viscoelasticity. CoRR Kaushik Bhattacharya, Burigede Liu, Andrew M. Stuart, Margaret Trautner
2022	J	`jnl`	Second Order Ensemble Langevin Method for Sampling and Inverse Problems. CoRR Ziming Liu, Andrew M. Stuart, Yixuan Wang
2022	J	`jnl`	The Cost-Accuracy Trade-Off In Operator Learning With Neural Networks. CoRR Maarten V. de Hoop, Daniel Zhengyu Huang, Elizabeth Qian, Andrew M. Stuart
2022	J	`jnl`	The Ensemble Kalman Filter in the Near-Gaussian Setting. CoRR J. A. Carrillo, F. Hoffmann, Andrew M. Stuart, U. Vaes
2021	J	`jnl`	A Framework for Machine Learning of Model Error in Dynamical Systems. CoRR Matthew E. Levine, Andrew M. Stuart
2021	J	`jnl`	Calibrate, emulate, sample. J. Comput. Phys. Emmet Cleary, Alfredo Garbuno-Inigo, Shiwei Lan, Tapio Schneider, Andrew M. Stuart
2021	J	`jnl`	Consensus Based Sampling. CoRR José Antonio Carrillo, Franca Hoffmann, Andrew M. Stuart, Urbain Vaes
2021	J	`jnl`	Consistency of empirical Bayes and kernel flow for hierarchical parameter estimation. Math. Comput. Yifan Chen, Houman Owhadi, Andrew M. Stuart
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`	Ensemble Inference Methods for Models With Noisy and Expensive Likelihoods. CoRR Andrew B. Duncan, Andrew M. Stuart, Marie-Therese Wolfram
2021	A*	`conf`	Fourier Neural Operator for Parametric Partial Differential Equations. ICLR Zongyi Li, Nikola Borislavov Kovachki, Kamyar Azizzadenesheli, Burigede Liu, Kaushik Bhattacharya, Andrew M. Stuart, Anima Anandkumar
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`	Solving and Learning Nonlinear PDEs with Gaussian Processes. CoRR Yifan Chen, Bamdad Hosseini, Houman Owhadi, Andrew M. Stuart
2021	J	`jnl`	Solving and learning nonlinear PDEs with Gaussian processes. J. Comput. Phys. Yifan Chen, Bamdad Hosseini, Houman Owhadi, Andrew M. Stuart
2021	J	`jnl`	The Random Feature Model for Input-Output Maps between Banach Spaces. SIAM J. Sci. Comput. Nicholas H. Nelsen, Andrew M. Stuart
2021	J	`jnl`	Unscented Kalman Inversion. CoRR Daniel Zhengyu Huang, Tapio Schneider, Andrew M. Stuart
2020	J	`jnl`	Consistency of Empirical Bayes And Kernel Flow For Hierarchical Parameter Estimation. CoRR Yifan Chen, Houman Owhadi, Andrew M. Stuart
2020	J	`jnl`	Consistency of Semi-Supervised Learning Algorithms on Graphs: Probit and One-Hot Methods. J. Mach. Learn. Res. Franca Hoffmann, Bamdad Hosseini, Zhi Ren, Andrew M. Stuart
2020	J	`jnl`	Diffusive Optical Tomography in the Bayesian Framework. Multiscale Model. Simul. Kit Newton, Qin Li, Andrew M. Stuart
2020	J	`jnl`	Drift Estimation of Multiscale Diffusions Based on Filtered Data. CoRR Assyr Abdulle, Giacomo Garegnani, Grigorios A. Pavliotis, Andrew M. Stuart, Andrea Zanoni
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`	Interacting Langevin Diffusions: Gradient Structure and Ensemble Kalman Sampler. SIAM J. Appl. Dyn. Syst. Alfredo Garbuno-Inigo, Franca Hoffmann, Wuchen Li, Andrew M. Stuart
2020	J	`jnl`	Inverse Optimal Transport. SIAM J. Appl. Math. Andrew M. Stuart, Marie-Therese Wolfram
2020	Misc	`conf`	Lessons learned from assimilating knowledge into machine learning to forecast and control glucose in a critical care setting. AMIA David J. Albers, Melike Sirlanci Tuysuzoglu, Matthew E. Levine, Caroline Der Nigoghossian, Andrew M. Stuart, Jan Claassen, Bruce J. Gluckman, George Hripcsak
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
2020	J	`jnl`	Posterior Consistency of Semi-Supervised Regression on Graphs. CoRR Andrea L. Bertozzi, Bamdad Hosseini, Hao Li, Kevin Miller, Andrew M. Stuart
2020	J	`jnl`	Reconciling Bayesian and Perimeter Regularization for Binary Inversion. SIAM J. Sci. Comput. Oliver R. A. Dunbar, Matthew M. Dunlop, Charles M. Elliott, Viet Ha Hoang, Andrew M. Stuart
2020	J	`jnl`	The Random Feature Model for Input-Output Maps between Banach Spaces. CoRR Nicholas H. Nelsen, Andrew M. Stuart
2020	J	`jnl`	Tikhonov Regularization within Ensemble Kalman Inversion. SIAM J. Numer. Anal. Neil K. Chada, Andrew M. Stuart, Xin T. Tong
2019	J	`jnl`	Analysis Of Momentum Methods. CoRR Nikola B. Kovachki, Andrew M. Stuart
2019	J	`jnl`	Consistency of semi-supervised learning algorithms on graphs: Probit and one-hot methods. CoRR Franca Hoffmann, Bamdad Hosseini, Zhi Ren, Andrew M. Stuart
2019	J	`jnl`	Parameter Estimation for Macroscopic Pedestrian Dynamics Models from Microscopic Data. SIAM J. Appl. Math. Susana N. Gomes, Andrew M. Stuart, Marie-Therese Wolfram
2019	J	`jnl`	Strong convergence rates of probabilistic integrators for ordinary differential equations. Stat. Comput. Han Cheng Lie, Andrew M. Stuart, Timothy John Sullivan
2019	—	`conf`	Uncertainty quantification for semi-supervised multi-class classification in image processing and ego-motion analysis of body-worn videos. Image Processing: Algorithms and Systems Yiling Qiao, Chang Shi, Chenjian Wang, Hao Li, Matt Haberland, Xiyang Luo, Andrew M. Stuart, Andrea L. Bertozzi
2018	J	`jnl`	Ensemble Kalman Inversion: A Derivative-Free Technique For Machine Learning Tasks. CoRR Nikola B. Kovachki, Andrew M. Stuart
2018	J	`jnl`	How Deep Are Deep Gaussian Processes? J. Mach. Learn. Res. Matthew M. Dunlop, Mark A. Girolami, Andrew M. Stuart, Aretha L. Teckentrup
2018	J	`jnl`	Large Data and Zero Noise Limits of Graph-Based Semi-Supervised Learning Algorithms. CoRR Matthew M. Dunlop, Dejan Slepcev, Andrew M. Stuart, Matthew Thorpe
2018	J	`jnl`	Mechanistic machine learning: how data assimilation leverages physiologic knowledge using Bayesian inference to forecast the future, infer the present, and phenotype. J. Am. Medical Informatics Assoc. David J. Albers, Matthew E. Levine, Andrew M. Stuart, Lena Mamykina, Bruce J. Gluckman, George Hripcsak
2018	J	`jnl`	Posterior consistency for Gaussian process approximations of Bayesian posterior distributions. Math. Comput. Andrew M. Stuart, Aretha L. Teckentrup
2018	J	`jnl`	Uncertainty Quantification in Graph-Based Classification of High Dimensional Data. SIAM/ASA J. Uncertain. Quantification Andrea L. Bertozzi, Xiyang Luo, Andrew M. Stuart, Konstantinos C. Zygalakis
2018	Misc	`conf`	Using mechanistic machine learning to forecast glucose and infer physiologic phenotypes in the ICU: what is possible and what are the challenges. AMIA David J. Albers, Matthew E. Levine, Andrew M. Stuart, Jan Claassen, Bruce J. Gluckman, George Hripcsak
2017	J	`jnl`	Analysis of the Ensemble Kalman Filter for Inverse Problems. SIAM J. Numer. Anal. Claudia Schillings, Andrew M. Stuart
2017	J	`jnl`	Gaussian Approximations for Probability Measures on R SIAM/ASA J. Uncertain. Quantification Yulong Lu, Andrew M. Stuart, Hendrik Weber
2017	J	`jnl`	Gaussian Approximations for Transition Paths in Brownian Dynamics. SIAM J. Math. Anal. Yulong Lu, Andrew M. Stuart, Hendrik Weber
2017	J	`jnl`	Geometric MCMC for infinite-dimensional inverse problems. J. Comput. Phys. Alexandros Beskos, Mark A. Girolami, Shiwei Lan, Patrick E. Farrell, Andrew M. Stuart
2017	J	`jnl`	Hierarchical Bayesian level set inversion. Stat. Comput. Matthew M. Dunlop, Marco A. Iglesias, Andrew M. Stuart
2017	J	`jnl`	Quasi-Monte Carlo and Multilevel Monte Carlo Methods for Computing Posterior Expectations in Elliptic Inverse Problems. SIAM/ASA J. Uncertain. Quantification Robert Scheichl, Andrew M. Stuart, Aretha L. Teckentrup
2017	J	`jnl`	Statistical analysis of differential equations: introducing probability measures on numerical solutions. Stat. Comput. Patrick R. Conrad, Mark A. Girolami, Simo Särkkä, Andrew M. Stuart, Konstantinos Zygalakis
2017	J	`jnl`	Uncertainty Quantification in the Classification of High Dimensional Data. CoRR Andrea L. Bertozzi, Xiyang Luo, Andrew M. Stuart, Konstantinos C. Zygalakis
2017	Misc	`conf`	Why predicting postprandial glucose using self-monitoring data is difficult. AMIA David J. Albers, Matthew E. Levine, Andrew M. Stuart, Bruce J. Gluckman, George Hripcsak
2016	Misc	`conf`	Using data assimilation to forecast post-meal glucose for patients with type 2 diabetes. AMIA David J. Albers, Matthew E. Levine, Andrew M. Stuart, George Hripcsak, Lena Mamykina
2015	J	`jnl`	A Multiscale Analysis of Diffusions on Rapidly Varying Surfaces. J. Nonlinear Sci. Andrew B. Duncan, Charles M. Elliott, Grigorios A. Pavliotis, Andrew M. Stuart
2015	J	`jnl`	Algorithms for Kullback-Leibler Approximation of Probability Measures in Infinite Dimensions. SIAM J. Sci. Comput. Francis J. Pinski, Gideon Simpson, Andrew M. Stuart, Hendrik Weber
2015	J	`jnl`	Kullback-Leibler Approximation for Probability Measures on Infinite Dimensional Spaces. SIAM J. Math. Anal. Francis J. Pinski, Gideon Simpson, Andrew M. Stuart, Hendrik Weber
2015	J	`jnl`	Long-Time Asymptotics of the Filtering Distribution for Partially Observed Chaotic Dynamical Systems. SIAM/ASA J. Uncertain. Quantification Daniel Sanz-Alonso, Andrew M. Stuart
2015	J	`jnl`	Sequential Monte Carlo methods for Bayesian elliptic inverse problems. Stat. Comput. Alexandros Beskos, Ajay Jasra, Ege A. Muzaffer, Andrew M. Stuart
2014	J	`jnl`	Analysis of the Gibbs Sampler for Hierarchical Inverse Problems. SIAM/ASA J. Uncertain. Quantification Sergios Agapiou, Johnathan M. Bardsley, Omiros Papaspiliopoulos, Andrew M. Stuart
2011	J	`jnl`	Evaluating Data Assimilation Algorithms CoRR Kody J. H. Law, Andrew M. Stuart
2011	J	`jnl`	Uncertainty Quantification and Weak Approximation of an Elliptic Inverse Problem. SIAM J. Numer. Anal. M. Dashti, Andrew M. Stuart
2010	J	`jnl`	Approximation of Bayesian Inverse Problems for PDEs. SIAM J. Numer. Anal. S. L. Cotter, M. Dashti, Andrew M. Stuart
2010	J	`jnl`	Convergence of Numerical Time-Averaging and Stationary Measures via Poisson Equations. SIAM J. Numer. Anal. Jonathan C. Mattingly, Andrew M. Stuart, Michael V. Tretyakov
2010	J	`jnl`	Inverse problems: A Bayesian perspective. Acta Numer. Andrew M. Stuart
2009	J	`jnl`	Calculating effective diffusivities in the limit of vanishing molecular diffusion. J. Comput. Phys. Grigorios A. Pavliotis, Andrew M. Stuart, Konstantinos C. Zygalakis
2009	J	`jnl`	Remarks on Drift Estimation for Diffusion Processes. Multiscale Model. Simul. Yvo Pokern, Andrew M. Stuart, Eric Vanden-Eijnden
2006	J	`jnl`	The Moment Map: Nonlinear Dynamics of Density Evolution via a Few Moments. SIAM J. Appl. Dyn. Syst. Dwight Barkley, Ioannis G. Kevrekidis, Andrew M. Stuart
2005	J	`jnl`	Analysis of White Noise Limits for Stochastic Systems with Two Fast Relaxation Times. Multiscale Model. Simul. Grigorios A. Pavliotis, Andrew M. Stuart
2003	J	`jnl`	Exponential Mean-Square Stability of Numerical Solutions to Stochastic Differential Equations. LMS J. Comput. Math. Desmond J. Higham, Xuerong Mao, Andrew M. Stuart
2003	J	`jnl`	White Noise Limits for Inertial Particles in a Random Field. Multiscale Model. Simul. Grigorios A. Pavliotis, Andrew M. Stuart
2002	J	`jnl`	Strong Convergence of Euler-Type Methods for Nonlinear Stochastic Differential Equations. SIAM J. Numer. Anal. Desmond J. Higham, Xuerong Mao, Andrew M. Stuart
2002	J	`jnl`	The dynamical behavior of the discontinuous Galerkin method and related difference schemes. Math. Comput. Donald J. Estep, Andrew M. Stuart
2001	J	`jnl`	Stiff Oscillatory Systems, Delta Jumps and White Noise. Found. Comput. Math. Begoña Cano, Andrew M. Stuart, Endre Süli, J. O. Warren
2000	J	`jnl`	A Perturbation Theory for Ergodic Markov Chains and Application to Numerical Approximations. SIAM J. Numer. Anal. Tony Shardlow, Andrew M. Stuart
1998	J	`jnl`	On the Solution of Convection-Diffusion Boundary Value Problems Using Equidistributed Grids. SIAM J. Sci. Comput. Chris J. Budd, George P. Koomullil, Andrew M. Stuart
1998	J	`jnl`	Space-Time Continuous Analysis of Waveform Relaxation for the Heat Equation. SIAM J. Sci. Comput. Martin J. Gander, Andrew M. Stuart
1997	J	`jnl`	Probabilistic and deterministic convergence proofs for software for initial value problems. Numer. Algorithms Andrew M. Stuart
1997	J	`jnl`	Waveform relaxation as a dynamical system. Math. Comput. Morten Bjørhus, Andrew M. Stuart
1994	J	`jnl`	Blow-up in a System of Partial Differential Equations with Conserved First Integral. Part II: Problems with Convection. SIAM J. Appl. Math. Chris J. Budd, J. William Dold, Andrew M. Stuart
1994	J	`jnl`	Model Problems in Numerical Stability Theory for Initial Value Problems. SIAM Rev. Andrew M. Stuart, A. R. Humphries
1993	J	`jnl`	Blowup in a Partial Differential Equation with Conserved First Integral. SIAM J. Appl. Math. Chris J. Budd, J. William Dold, Andrew M. Stuart
1993	J	`jnl`	The Numerical Computation of Heteroclinic Connections in Systems of Gradient Partial Differential Equations. SIAM J. Appl. Math. Fengshan Bai, Alastair Spence, Andrew M. Stuart
1991	J	`jnl`	The Dynamics of the Theta Method. SIAM J. Sci. Comput. Andrew M. Stuart, A. T. Peplow
1989	J	`jnl`	Nonlinear Instability in Dissipative Finite Difference Schemes. SIAM Rev. Andrew M. Stuart