Stuart, Andrew M

(orcid 0000-0001-9091-7266)

Article(s) from CaltechAUTHORS

• Schneider, Tapio; Stuart, Andrew M. et al. (2022) Ensemble Kalman inversion for sparse learning of dynamical systems from time-averaged data Journal of Computational Physics; Vol. 470; https://doi.org/10.1016/j.jcp.2022.111559

• Dunbar, Oliver R. A.; Howland, Michael F. et al. (2022) Ensemble-Based Experimental Design for Targeting Data Acquisition to Inform Climate Models Journal of Advances in Modeling Earth Systems; Vol. 14; No. 9; https://doi.org/10.1029/2022ms002997

• Huang, Daniel Zhengyu; Schneider, Tapio et al. (2022) Iterated Kalman methodology for inverse problems Journal of Computational Physics; Vol. 463; https://doi.org/10.1016/j.jcp.2022.111262

• Dunbar, Oliver R. A.; Duncan, Andrew B. et al. (2022) Ensemble Inference Methods for Models With Noisy and Expensive Likelihoods SIAM Journal on Applied Dynamical Systems; Vol. 21; No. 2; https://doi.org/10.1137/21M1410853

• Carrillo, J. A.; Hoffmann, F. et al. (2022) Consensus-based sampling Studies in Applied Mathematics; Vol. 148; No. 3; https://doi.org/10.1111/sapm.12470

• Kovachki, Nikola; Liu, Burigede et al. (2022) Multiscale modeling of materials: Computing, data science, uncertainty and goal-oriented optimization Mechanics of Materials; Vol. 165; https://doi.org/10.1016/j.mechmat.2021.104156

• Pavliotis, G. A.; Stuart, A. M. et al. (2022) Derivative-Free Bayesian Inversion Using Multiscale Dynamics SIAM Journal on Applied Dynamical Systems; Vol. 21; No. 1; https://doi.org/10.1137/21M1397416

• Liu, Burigede; Kovachki, Nikola et al. (2022) A learning-based multiscale method and its application to inelastic impact problems Journal of the Mechanics and Physics of Solids; Vol. 158; https://doi.org/10.1016/j.jmps.2021.104668

• Hoffmann, Franca; Hosseini, Bamdad et al. (2022) Spectral analysis of weighted Laplacians arising in data clustering Applied and Computational Harmonic Analysis; Vol. 56; https://doi.org/10.1016/j.acha.2021.07.004

• Chen, Yifan; Hosseini, Bamdad et al. (2021) Solving and learning nonlinear PDEs with Gaussian processes Journal of Computational Physics; Vol. 447; https://doi.org/10.1016/j.jcp.2021.110668

• Abdulle, Assyr; Garegnani, Giacomo et al. (2021) Drift Estimation of Multiscale Diffusions Based on Filtered Data Foundations of Computational Mathematics; https://doi.org/10.1007/s10208-021-09541-9 (In Press)

• Bertozzi, Andrea L.; Hosseini, Bamdad et al. (2021) Posterior consistency of semi-supervised regression on graphs Inverse Problems; Vol. 37; No. 10; https://doi.org/10.1088/1361-6420/ac1e80

• Nelsen, Nicholas H.; Stuart, Andrew M. (2021) The Random Feature Model for Input-Output Maps between Banach Spaces SIAM Journal on Scientific Computing; Vol. 43; No. 5; https://doi.org/10.1137/20M133957X

• Dunbar, Oliver R. A.; Garbuno-Inigo, Alfredo et al. (2021) Calibration and Uncertainty Quantification of Convective Parameters in an Idealized GCM Journal of Advances in Modelling Earth Systems; Vol. 13; No. 9; https://doi.org/10.1029/2020MS002454

• Burov, Dmitry; Giannakis, Dimitrios et al. (2021) Kernel Analog Forecasting: Multiscale Test Problems Multiscale Modeling and Simulation; Vol. 19; No. 2; https://doi.org/10.1137/20M1338289

• Chen, Yifang; Owhadi, Houman et al. (2021) Consistency of empirical Bayes and kernel flow for hierarchical parameter estimation Mathematics of Computation; Vol. 90; https://doi.org/10.1090/mcom/3649

• Cleary, Emmet; Garbuno-Inigo, Alfredo et al. (2021) Calibrate, emulate, sample Journal of Computational Physics; Vol. 424; https://doi.org/10.1016/j.jcp.2020.109716

• Kovachki, Nikola B.; Stuart, Andrew M. (2021) Continuous Time Analysis of Momentum Methods Journal of Machine Learning Research; Vol. 22; No. 17; https://resolver.caltech.edu/CaltechAUTHORS:20210503-091850360

• Dunlop, Matthew M.; Slepčev, Dejan et al. (2020) Large Data and Zero Noise Limits of Graph-Based Semi-Supervised Learning Algorithms Applied and Computational Harmonic Analysis; Vol. 49; No. 2; https://doi.org/10.1016/j.acha.2019.03.005

• Dunbar, Oliver R. A.; Dunlop, Matthew M. et al. (2020) Reconciling Bayesian and Perimeter Regularization for Binary Inversion SIAM Journal on Scientific Computing; Vol. 42; No. 4; https://doi.org/10.1137/18M1179559

• Hoffmann, Franca; Hosseini, Bamdad et al. (2020) Consistency of Semi-Supervised Learning Algorithms on Graphs: Probit and One-Hot Methods Journal of Machine Learning Research; Vol. 21; https://doi.org/10.48550/arXiv.1906.07658

• Seylabi, Elnaz; Stuart, Andrew M. et al. (2020) Site Characterization at Downhole Arrays by Joint Inversion of Dispersion Data and Acceleration Time Series Bulletin of the Seismological Society of America; Vol. 110; No. 3; https://doi.org/10.1785/0120190256

• Newton, Kit; Li, Qin et al. (2020) Diffusive optical tomography in the Bayesian framework Multiscale Modeling and Simulation; Vol. 18; No. 2; https://doi.org/10.1137/19M1247346

• Chada, Neil K.; Stuart, Andrew M. et al. (2020) Tikhonov Regularization Within Ensemble Kalman Inversion SIAM Journal on Numerical Analysis; Vol. 58; No. 2; https://doi.org/10.1137/19M1242331

• Stuart, Andrew M.; Wolfram, Marie-Therese (2020) Inverse optimal transport SIAM Journal on Applied Mathematics; Vol. 80; No. 1; https://doi.org/10.1137/19M1261122

• Garbuno-Inigo, Alfredo; Hoffmann, Franca et al. (2020) Interacting Langevin Diffusions: Gradient Structure and Ensemble Kalman Sampler SIAM Journal on Applied Dynamical Systems; Vol. 19; No. 1; https://doi.org/10.1137/19M1251655

• Lie, Han Cheng; Stuart, A. M. et al. (2019) Strong convergence rates of probabilistic integrators for ordinary differential equations Statistics and Computing; Vol. 29; No. 6; https://doi.org/10.1007/s11222-019-09898-6

• Kuntz, Juan; Ottobre, Michela et al. (2019) Diffusion Limit For The Random Walk Metropolis Algorithm Out Of stationarity Annales De l'Institut Henri Poincaré - Probabilitiés et Statistiques; Vol. 55; No. 3; https://doi.org/10.1214/18-AIHP929

• Kovachki, Nikola B.; Stuart, Andrew M. (2019) Ensemble Kalman Inversion: A Derivative-Free Technique For Machine Learning Tasks Inverse Problems; Vol. 35; No. 9; https://doi.org/10.1088/1361-6420/ab1c3a

• Albers, David J.; Blancquart, Paul-Adrien et al. (2019) Ensemble Kalman Methods With Constraints Inverse Problems; Vol. 35; No. 9; https://doi.org/10.1088/1361-6420/ab1c09

• Kelly, David; Stuart, Andrew M. (2019) Ergodicity and Accuracy of Optimal Particle Filters for Bayesian Data Assimilation Chinese Annals of Mathematics, Series B; Vol. 40; No. 5; https://doi.org/10.1007/s11401-019-0161-5

• Gomes, Susana N.; Stuart, Andrew M. et al. (2019) Parameter estimation for macroscopic pedestrian dynamics models from microscopic data SIAM Journal on Applied Mathematics; Vol. 79; No. 4; https://doi.org/10.1137/18M1215980

• Qiao, Yiling; Shi, Chang et al. (2019) Uncertainty quantification for semi-supervised multi-class classification in image processing and ego-motion analysis of body-worn videos Electronic Imaging; Vol. 2019; In: IS&T International Symposium on Electronic Imaging 2019: Image Processing: Algorithms and Systems XVII, 13-17 January 2019, Burlingame, CA https://doi.org/10.2352/ISSN.2470-1173.2019.11.IPAS-264

• Albers, David J.; Levine, Matthew E. et al. (2018) Mechanistic machine learning: how data assimilation leverages physiologic knowledge using Bayesian inference to forecast the future, infer the present, and phenotype Journal of the American Medical Informatics Association; Vol. 25; No. 10; https://doi.org/10.1093/jamia/ocy106

• Dunlop, Matthew M.; Girolami, Mark A. et al. (2018) How Deep Are Deep Gaussian Processes? Journal of Machine Learning Research; Vol. 19; No. 54; https://doi.org/10.48550/arXiv.1711.11280

• Kuntz, Juan; Ottobre, Michela et al. (2018) Non-stationary phase of the MALA algorithm Stochastics and Partial Differential Equations: Analysis and Computations; Vol. 6; No. 3; https://doi.org/10.1007/s40072-018-0113-1

• Chada, Neil K.; Iglesias, Marco A. et al. (2018) Parameterizations for ensemble Kalman inversion Inverse Problems; Vol. 34; No. 5; https://doi.org/10.1088/1361-6420/aab6d9

• Bertozzi, Andrea L.; Luo, Xiyang et al. (2018) Uncertainty Quantification in Graph-Based Classification of High Dimensional Data SIAM/ASA Journal on Uncertainty Quantification; Vol. 6; No. 2; https://doi.org/10.1137/17M1134214

• Bréhier, Charles-Edouard; Hairer, Martin et al. (2018) Weak error estimates for trajectories of SPDEs for Spectral Galerkin discretization Journal of Computational Mathematics; Vol. 36; No. 2; https://doi.org/10.4208/jcm.1607-m2016-0539

• Stuart, Andrew M.; Teckentrup, Aretha L. (2018) Posterior consistency for Gaussian process approximations of Bayesian posterior distributions Mathematics of Computation; Vol. 87; No. 310; https://doi.org/10.1090/mcom/3244

• Calvetti, Daniela; Dunlop, Matthew et al. (2018) Iterative Updating of Model Error for Bayesian Inversion Inverse Problems; Vol. 34; No. 2; https://doi.org/10.1088/1361-6420/aaa34d

• Schillings, C.; Stuart, A. M. (2018) Convergence analysis of ensemble Kalman inversion: the linear, noisy case Applicable Analysis: An International Journal; Vol. 97; No. 1; https://doi.org/10.1080/00036811.2017.1386784

• Schneider, Tapio; Lan, Shiwei et al. (2017) Earth System Modeling 2.0: A Blueprint for Models That Learn From Observations and Targeted High-Resolution Simulations Geophysical Research Letters; Vol. 44; No. 24; https://doi.org/10.1002/2017GL076101

• Lu, Yulong; Stuart, Andrew et al. (2017) Gaussian Approximations for Probability Measures on R^d SIAM/ASA Journal on Uncertainty Quantification; Vol. 5; No. 1; https://doi.org/10.1137/16M1105384

• Dunlop, Matthew M.; Iglesias, Marco A. et al. (2017) Hierarchical Bayesian level set inversion Statistics and Computing; Vol. 27; No. 6; https://doi.org/10.1007/s11222-016-9704-8

• Sanz-Alonso, Daniel; Stuart, Andrew M. (2017) Gaussian Approximations of Small Noise Diffusions in Kullback-Leibler Divergence Communications in Mathematical Sciences; Vol. 15; No. 7; https://doi.org/10.4310/CMS.2017.v15.n7.a13

• Agapiou, S.; Papaspiliopoulos, O. et al. (2017) Importance Sampling: Intrinsic Dimension and Computational Cost Statistical Science; Vol. 32; No. 3; https://doi.org/10.1214/17-STS611

• Lu, Yulong; Stuart, Andrew et al. (2017) Gaussian Approximations for Transition Paths in Brownian Dynamics SIAM Journal on Mathematical Analysis; Vol. 49; No. 4; https://doi.org/10.1137/16M1071845

• Conrad, Patrick R.; Girolami, Mark et al. (2017) Statistical analysis of differential equations: introducing probability measures on numerical solutions Statistics and Computing; Vol. 27; No. 4; https://doi.org/10.1007/s11222-016-9671-0

• Schillings, Claudia; Stuart, Andrew M. (2017) Analysis of the ensemble Kalman filter for inverse problems SIAM Journal on Numerical Analysis; Vol. 55; No. 3; https://doi.org/10.1137/16M105959X

• Scheichl, R.; Stuart, A. M. et al. (2017) Quasi-Monte Carlo and Multilevel Monte Carlo Methods for Computing Posterior Expectations in Elliptic Inverse Problems SIAM/ASA Journal on Uncertainty Quantification; Vol. 5; No. 1; https://doi.org/10.1137/16M1061692

• Beskos, Alexandros; Girolami, Mark et al. (2017) Geometric MCMC for Infinite-Dimensional Inverse Problems Journal of Computational Physics; Vol. 335; https://doi.org/10.1016/j.jcp.2016.12.041

• Lee, Wongjung; Stuart, Andrew (2017) Derivation and analysis of simplified filters Communications in Mathematical Sciences; Vol. 15; No. 2; https://doi.org/10.4310/CMS.2017.v15.n2.a6

• Iglesias, Marco A.; Lin, Kui et al. (2017) Filter Based Methods For Statistical Linear Inverse Problems Communications in Mathematical Sciences; Vol. 15; No. 7; https://doi.org/10.4310/CMS.2017.v15.n7.a4

• Dunlop, Matthew M.; Stuart, Andrew M. (2016) The Bayesian formulation of EIT: Analysis and algorithms Inverse Problems and Imaging; Vol. 10; No. 4; https://doi.org/10.3934/ipi.2016030

• Dunlop, M. M.; Stuart, A. M. (2016) MAP estimators for piecewise continuous inversion Inverse Problems; Vol. 32; No. 10; https://doi.org/10.1088/0266-5611/32/10/105003

• Law, K. J. H.; Sanz-Alonso, D. et al. (2016) Filter accuracy for the Lorenz 96 model: fixed versus adaptive observation operators Physica D: Nonlinear Phenomena; Vol. 325; https://doi.org/10.1016/j.physd.2015.12.008

• Bühlmann, Peter; Stuart, A. M. (2016) Mathematics, Statistics and Data Science In: EMS Newsletter; EMS Newsletter; Vol. 100; https://resolver.caltech.edu/CaltechAUTHORS:20161111-103206810

• Ottobre, Michela; Pillai, Natesh S. et al. (2016) A Function Space HMC Algorithm With Second Order Langevin Diffusion Limit Bernoulli; Vol. 22; No. 1; https://doi.org/10.3150/14-BEJ621

• Iglesias, Marco A.; Lu, Yulong et al. (2016) A Bayesian Level Set Method for Geometric Inverse Problems Interfaces and Free Boundaries; Vol. 18; No. 2; https://doi.org/10.4171/IFB/362

• Sanz-Alonso, Daniel; Stuart, Andrew M. (2015) Long-Time Asymptotics of the Filtering Distribution for Partially Observed Chaotic Dynamical Systems SIAM/ASA Journal on Uncertainty Quantification; Vol. 3; No. 1; https://doi.org/10.1137/140997336

• Beskos, Alexandros; Jasra, Ajay et al. (2015) Sequential Monte Carlo methods for Bayesian elliptic inverse problems Statistics and Computing; Vol. 25; No. 4; https://doi.org/10.1007/s11222-015-9556-7

• Duncan, A. B.; Elliott, C. M. et al. (2015) A Multiscale Analysis of Diffusions on Rapidly Varying Surfaces Journal of Nonlinear Science; Vol. 25; No. 2; https://doi.org/10.1007/s00332-015-9237-x

• Pinski, F. J.; Simpson, G. et al. (2015) Algorithms for Kullback--Leibler Approximation of Probability Measures in Infinite Dimensions SIAM Journal on Scientific Computing; Vol. 37; No. 6; https://doi.org/10.1137/14098171X

• Reich, Sebastian; Stuart, Andrew M. (2015) Data Assimilation: New Challenges in Random and Stochastic Dynamical Systems In: SIAM News; SIAM News; Vol. 48; No. 8 & 9; In: 2015 SIAM Conference on Applications of Dynamical Systems, May 17-21, 2015, Snowbird, UT https://resolver.caltech.edu/CaltechAUTHORS:20161111-104214792

• Pinski, F. J.; Simpson, G. et al. (2015) Kullback-Leibler approximation for probability measures on infinite dimensional spaces SIAM Journal on Mathematical Analysis; Vol. 47; No. 6; https://doi.org/10.1137/140962802

• Hairer, Martin; Stuart, Andrew M. et al. (2014) Spectral gaps for a Metropolis–Hastings algorithm in infinite dimensions Annals of Applied Probability; Vol. 24; No. 6; https://doi.org/10.1214/13-AAP982

• Iglesias, Marco A.; Lin, Kui et al. (2014) Well-posed Bayesian geometric inverse problems arising in subsurface flow Inverse Problems; Vol. 30; No. 11; https://doi.org/10.1088/0266-5611/30/11/114001

• Kelly, D. B. T.; Law, K. J. H. et al. (2014) Well-posedness and accuracy of the ensemble Kalman filter in discrete and continuous time Nonlinearity; Vol. 27; No. 10; https://doi.org/10.1088/0951-7715/27/10/2579

• Agapiou, Sergios; Bardsley, Jonathan M. et al. (2014) Analysis of the Gibbs Sampler for Hierarchical Inverse Problems SIAM/ASA Journal on Uncertainty Quantification; Vol. 2; No. 1; https://doi.org/10.1137/130944229

• Agapiou, Sergios; Stuart, Andrew M. et al. (2014) Bayesian posterior contraction rates for linear severely ill-posed inverse problems Journal of Inverse and Ill-posed Problems; Vol. 22; No. 3; https://doi.org/10.1515/jip-2012-0071

• Hoang, Viet Ha; Law, Kody J. H. et al. (2014) Determining white noise forcing from Eulerian observations in the Navier-Stokes equation Stochastic Partial Differential Equations: Analysis and Computations; Vol. 2; No. 2; https://doi.org/10.1007/s40072-014-0028-4

• Pillai, Natesh S.; Stuart, Andrew M. et al. (2014) Noisy gradient flow from a random walk in Hilbert space Stochastic Partial Differential Equations: Analysis and Computations; Vol. 2; No. 2; https://doi.org/10.1007/s40072-014-0029-3

• Law, Kody; Shukla, Abishek et al. (2014) Analysis of the 3DVAR filter for the partially observed Lorenz'63 model Discrete and Continuous Dynamical Systems A; Vol. 34; No. 3; https://doi.org/10.3934/dcds.2014.34.1061

• Iglesias, Marco; Stuart, Andrew M. (2014) Inverse Problems and Uncertainty Quantification In: SIAM News; SIAM News; In: 2014 SIAM Conference on Uncertainty Quantification, March 31-April 3, 2014, Savannah, GA https://resolver.caltech.edu/CaltechAUTHORS:20161111-105218524

• Beskos, Alexandros; Pillai, Natesh et al. (2013) Optimal tuning of the hybrid Monte Carlo algorithm Bernoulli; Vol. 19; No. 5A; https://doi.org/10.3150/12-BEJ414

• Agapiou, Sergios; Larsson, Stig et al. (2013) Posterior contraction rates for the Bayesian approach to linear ill-posed inverse problems Stochastic Processes and their Applications; Vol. 123; No. 10; https://doi.org/10.1016/j.spa.2013.05.001

• Dashti, M.; Law, K. J. H. et al. (2013) MAP estimators and their consistency in Bayesian nonparametric inverse problems Inverse Problems; Vol. 29; No. 9; https://doi.org/10.1088/0266-5611/29/9/095017

• Cotter, S. L.; Roberts, G. O. et al. (2013) MCMC Methods for Functions: Modifying Old Algorithms to Make Them Faster Statistical Science; Vol. 28; No. 3; https://doi.org/10.1214/13-STS421

• Hoang, Viet Ha; Schwab, Christoph et al. (2013) Complexity analysis of accelerated MCMC methods for Bayesian inversion Inverse Problems; Vol. 29; No. 8; https://doi.org/10.1088/0266-5611/29/8/085010

• Blömker, D.; Law, K. et al. (2013) Accuracy and stability of the continuous-time 3DVAR filter for the Navier–Stokes equation Nonlinearity; Vol. 26; No. 8; https://doi.org/10.1088/0951-7715/26/8/2193

• Iglesias, Marco A.; Law, Kody et al. (2013) Evaluation of Gaussian approximations for data assimilation in reservoir models Computational Geosciences; Vol. 17; No. 5; https://doi.org/10.1007/s10596-013-9359-x

• Iglesias, Marco A.; Law, Kody J. H. et al. (2013) Ensemble Kalman methods for inverse problems Inverse Problems; Vol. 29; No. 4; https://doi.org/10.1088/0266-5611/29/4/045001

• Brett, C. E. A.; Lam, K. F. et al. (2013) Accuracy and stability of filters for dissipative PDEs Physica D: Nonlinear Phenomena; Vol. 245; No. 1; https://doi.org/10.1016/j.physd.2012.11.005

• Pokern, Y.; Stuart, A. M. et al. (2013) Posterior consistency via precision operators for Bayesian nonparametric drift estimation in SDEs Stochastic Processes and their Applications; Vol. 123; No. 2; https://doi.org/10.1016/j.spa.2012.08.010

• Pillai, Natesh S.; Stuart, Andrew M. et al. (2012) Optimal scaling and diffusion limits for the Langevin algorithm in high dimensions Annals of Applied Probability; Vol. 22; No. 6; https://doi.org/10.1214/11-AAP828

• Law, K. J. H.; Stuart, A. M. (2012) Evaluating Data Assimilation Algorithms Monthly Weather Review; Vol. 140; https://doi.org/10.1175/MWR-D-11-00257.1

• Papaspiliopoulos, Omiros; Pokern, Yvo et al. (2012) Nonparametric estimation of diffusions: a differential equations approach Biometrika; Vol. 99; No. 3; https://doi.org/10.1093/biomet/ass034

• Mattingly, Jonathan C.; Pillai, Natesh S. et al. (2012) Diffusion limits of the random walk Metropolis algorithm in high dimensions Annals of Applied Probability; Vol. 22; No. 3; https://doi.org/10.1214/10-AAP754

• Dashti, Masoumeh; Harris, Stephen J. et al. (2012) Besov priors for Bayesian inverse problems Inverse Problems and Imaging; Vol. 6; No. 2; https://doi.org/10.3934/ipi.2012.6.183

• Schwab, C.; Stuart, A. M. (2012) Sparse determinisitc approximation of Bayesian inverse problems Inverse Problems; Vol. 28; No. 4; https://doi.org/10.1088/0266-5611/28/4/045003

• Pinski, F. J.; Stuart, A. M. et al. (2012) Γ-Limit for Transition Paths of Maximal Probability Journal of Statistical Physics; Vol. 146; No. 5; https://doi.org/10.1007/s10955-012-0443-8

• Cotter, S. L.; Dashti, M. et al. (2012) Variational data assimilation using targetted random walks International Journal for Numerical Methods in Fluids; Vol. 68; No. 4; https://doi.org/10.1002/fld.2510

• Dashti, M.; Stuart, A. M. (2011) Uncertainty Quantification and Weak Approximation of an Elliptic Inverse Problem SIAM Journal on Numerical Analysis; Vol. 49; No. 6; https://doi.org/10.1137/100814664

• Beskos, A.; Pinski, F. J. et al. (2011) Hybrid Monte Carlo on Hilbert spaces Stochastic Processes and their Applications; Vol. 121; No. 10; https://doi.org/10.1016/j.spa.2011.06.003

• Lee, Wonjung; McDougall, D. et al. (2011) Kalman filtering and smoothing for linear wave equations with model error Inverse Problems; Vol. 27; No. 9; https://doi.org/10.1088/0266-5611/27/9/095008

• Melbourne, I.; Stuart, A. M. (2011) A note on diffusion limits of chaotic skew-product flows Nonlinearity; Vol. 24; No. 4; https://doi.org/10.1088/0951-7715/24/4/018

• Hairer, Martin; Stuart, Andrew M. et al. (2011) Sampling conditioned hypoelliptic diffusions Annals of Applied Probability; Vol. 21; No. 2; https://doi.org/10.1214/10-AAP708

• Fearnhead, Paul; Papaspiliopoulos, Omiros et al. (2010) Random-weight particle filtering of continuous time processes Journal of the Royal Statistical Society: Series B; Vol. 72; No. 4; https://doi.org/10.1111/j.1467-9868.2010.00744.x

• Fearnhead, Paul; Papaspiliopoulos, Omiros et al. (2010) Random-weight particle filtering of continuous time processes Statistical Methodology; Vol. 72; No. 4; https://doi.org/10.1111/j.1467-9868.2010.00744.x

• Pinski, F. J.; Stuart, A. M. (2010) Transition paths in molecules at finite temperature Journal of Chemical Physics; Vol. 132; No. 18; https://doi.org/10.1063/1.3391160

• Mattingly, J. C.; Stuart, A. M. et al. (2010) Convergence of numerical time-averaging and stationary measures via Poisson equations SIAM Journal of Numerical Analysis; Vol. 48; No. 2; https://doi.org/10.1137/090770527

• Stuart, A. M. (2010) Inverse problems: A Bayesian perspective In: Acta Numerica; Acta Numerica; Vol. 19; https://doi.org/10.1017/S0962492910000061

• Cotter, S. L.; Dashti, M. et al. (2010) Approximation of Bayesian Inverse Problems for PDEs SIAM Journal on Numerical Analysis; Vol. 48; No. 1; https://doi.org/10.1137/090770734

• Cotter, S. L.; Dashti, M. et al. (2009) Bayesian inverse problems for functions and applications to fluid mechanics Inverse Problems; Vol. 25; No. 11; https://doi.org/10.1088/0266-5611/25/11/115008

• Pokern, Yvo; Stuart, Andrew M. et al. (2009) Remarks on Drift Estimation for Diffusion Processes Multiscale Modeling and Simulation; Vol. 8; No. 1; https://doi.org/10.1137/070694806

• Papavasiliou, A.; Pavliotis, G. A. et al. (2009) Maximum likelihood drift estimation for multiscale diffusions Stochastic Processes and their Applications; Vol. 119; No. 10; https://doi.org/10.1016/j.spa.2009.05.003

• Beskos, Alexandros; Roberts, Gareth et al. (2009) Optimal scalings for local Metropolis–Hastings chains on nonproduct targets in high dimensions Annals of Applied Probability; Vol. 19; No. 3; https://doi.org/10.1214/08-AAP563

• Pavliotis, G. A.; Stuart, A. M. et al. (2009) Calculating effective diffusivities in the limit of vanishing molecular diffusion Journal of Computational Physics; Vol. 288; No. 4; https://doi.org/10.1016/j.jcp.2008.10.014

• Pokern, Yvo; Stuart, Andrew M. et al. (2009) Parameter estimation for partially observed hypo-elliptic diffusions Journal of the Royal Society: Series B (Statistical Methodology); Vol. 71; No. 1; https://doi.org/10.1111/j.1467-9868.2008.00689.x

• Pokern, Yvo; Stuart, Andrew M. et al. (2009) Parameter estimation for partially observed hypoelliptic diffusions Statistical Methodology; Vol. 71; Series B; No. 1; https://doi.org/10.1111/j.1467-9868.2008.00689.x

• Beskos, Alexandros; Roberts, Gareth et al. (2008) MCMC Methods for Diffusion Bridges Stochastics and Dynamics; Vol. 8; No. 3; https://doi.org/10.1142/S0219493708002378

• Apte, A.; Jones, C. K. R. T. et al. (2008) Data assimilation: Mathematical and statistical perspectives International Journal for Numerical Methods in Fluids; Vol. 56; No. 8; https://doi.org/10.1002/fld.1698

• Apte, A.; Jones, C. K. R. T. et al. (2008) A Bayesian approach to Lagrangian data assimilation Tellus A; Vol. 60; No. 2; https://doi.org/10.1111/j.1600-0870.2007.00295.x

• Lamba, H.; Mattingly, J. C. et al. (2007) An adaptive Euler-Maruyama scheme for SDEs: convergence and stability IMA Journal of Numerical Analysis; Vol. 27; No. 3; https://doi.org/10.1093/imanum/drl032

• Apte, A.; Hairer, M. et al. (2007) Sampling the posterior: An approach to non-Gaussian data assimilation Physica D; Vol. 230; No. 1-2; https://doi.org/10.1016/j.physd.2006.06.009

• Pavliotis, G. A.; Stuart, A. M. (2007) Parameter Estimation for Multiscale Diffusions Journal of Statistical Physics; Vol. 127; No. 4; https://doi.org/10.1007/s10955-007-9300-6

• Hairer, M.; Stuart, A. M. et al. (2007) Analysis of SPDEs arising in path sampling part II: The nonlinear case Annals of Applied Probability; Vol. 17; No. 5/6; https://doi.org/10.1214/07-AAP441

• Pavliotis, G. A.; Stuart, A. M. et al. (2007) Homogenization for inertial particles in a random flow Communications in Mathematical Sciences; Vol. 5; No. 3; https://doi.org/10.4310/CMS.2007.v5.n3.a1

• Barkley, D.; Kevrekidis, I. G. et al. (2006) The Moment Map: Nonlinear Dynamics of Density Evolution via a Few Moments SIAM Journal on Applied Dynamical Systems; Vol. 5; No. 3; https://doi.org/10.1137/050638667

• Pavliotis, G. A.; Stuart, A. M. (2005) Analysis of White Noise Limits for Stochastic Systems with Two Fast Relaxation Times Multiscale Modeling and Simulation; Vol. 4; No. 1; https://doi.org/10.1137/040610507

• Pavliotis, G. A.; Stuart, A. M. (2005) Periodic homogenization for inertial particles Physica D; Vol. 204; No. 3-4; https://doi.org/10.1016/j.physd.2005.04.011

• Hairer, M.; Stuart, A. M. et al. (2005) Analysis of SPDEs arising in path sampling. Part I: The Gaussian case Communications in Mathematical Sciences; Vol. 3; No. 4; https://doi.org/10.4310/CMS.2005.v3.n4.a8

• Kupferman, R.; Stuart, A. M. (2004) Fitting SDE models to nonlinear Kac–Zwanzig heat bath models Physica D; Vol. 199; No. 3-4; https://doi.org/10.1016/j.physd.2004.04.011

• Stuart, Andrew M.; Voss, Jochen et al. (2004) Conditional Path Sampling of SDEs and the Langevin MCMC Method Communications in Mathematical Sciences; Vol. 2; No. 4; https://doi.org/10.4310/CMS.2004.v2.n4.a7

• Givon, Dror; Kupferman, Raz et al. (2004) Extracting macroscopic dynamics: model problems and algorithms Nonlinearity; Vol. 17; No. 6; In: 2002 Ron Diperna Memorial Lecture, 7 February 2002, Berkeley, CA https://doi.org/10.1088/0951-7715/17/6/R01

• Kupferman, R.; Pavliotis, G. A. et al. (2004) Itô versus Stratonovich white-noise limits for systems with inertia and colored multiplicative noise Physical Review E; Vol. 70; No. 3; https://doi.org/10.1103/PhysRevE.70.036120

• Pavliotis, G. A.; Stuart, A. M. (2003) White Noise Limits for Inertial Particles in a Random Field Multiscale Modeling and Simulation; Vol. 1; No. 4; https://doi.org/10.1137/S1540345903421076

• Huisinga, Wilhelm; Schütte, Christof et al. (2003) Extracting macroscopic stochastic dynamics: Model problems Communications on Pure and Applied Mathematics; Vol. 56; No. 2; https://doi.org/10.1002/cpa.10057

• Higham, Desmond J.; Mao, Xuerong et al. (2003) Exponential Mean-Square Stability of Numerical Solutions to Stochastic Differential Equations LMS Journal of Computation and Mathematics; Vol. 6; https://doi.org/10.1112/S1461157000000462

• Kupferman, R.; Stuart, A. M. et al. (2002) Long-Term Behavior of Large Mechanical Systems with Random Initial Data Stochastics and Dynamics; Vol. 02; No. 04; https://doi.org/10.1142/S0219493702000571

• Sigurgeirsson, H.; Stuart, A. M. (2002) A model for preferential concentration Physics of Fluids; Vol. 14; No. 12; https://doi.org/10.1063/1.1517603

• Mattingly, J. C.; Stuart, A. M. et al. (2002) Ergodicity for SDEs and approximations: locally Lipschitz vector fields and degenerate noise Stochastic Processes and their Applications; Vol. 101; No. 2; https://doi.org/10.1016/S0304-4149(02)00150-3

• Higham, Desmond J.; Mao, Xuerong et al. (2002) Strong Convergence of Euler-Type Methods for Nonlinear Stochastic Differential Equations SIAM Journal on Numerical Analysis; Vol. 40; No. 3; https://doi.org/10.1137/S0036142901389530

• Estep, Donald J.; Stuart, Andrew M. (2002) The dynamical behavior of the discontinuous Galerkin method and related difference schemes Mathematics of Computation; Vol. 71; No. 239; https://doi.org/10.1090/S0025-5718-01-01364-3

• Sigurgeirsson, H.; Stuart, A. M. (2002) Inertial Particles in a Random Field Stochastics and Dynamics; Vol. 02; No. 02; https://doi.org/10.1142/S021949370200042X

• Mattingly, J. C.; Stuart, A. M. (2002) Geometric Ergodicity of Some Hypo-Elliptic Diffusions for Particle Motions Markov Processes And Related Fields; Vol. 8; No. 2; https://resolver.caltech.edu/CaltechAUTHORS:20170613-125012320

• Sigurgeirsson, Hersir; Stuart, Andrew et al. (2001) Algorithms for Particle-Field Simulations with Collisions Journal of Computational Physics; Vol. 172; No. 2; https://doi.org/10.1006/jcph.2001.6858

• Cano, B.; Stuart, A. M. (2001) Underresolved Simulations of Heat Baths Journal of Computational Physics; Vol. 169; No. 1; https://doi.org/10.1006/jcph.2001.6722

• Cano, B.; Stuart, A. M. et al. (2001) Stiff Oscillatory Systems, Delta Jumps and White Noise Foundations of Computational Mathematics; Vol. 1; No. 1; https://doi.org/10.1007/s10208001002

• Shardlow, T.; Stuart, A. M. (2000) A Perturbation Theory for Ergodic Markov Chains and Application to Numerical Approximations SIAM Journal on Numerical Analysis; Vol. 37; No. 4; https://doi.org/10.1137/S0036142998337235

• Stuart, A. M.; Warren, J. O. (1999) Analysis and Experiments for a Computational Model of a Heat Bath Journal of Statistical Physics; Vol. 97; No. 3/4; https://doi.org/10.1023/A:1004667325896

• Sanz-Serna, J. M.; Stuart, A. M. (1999) Ergodicity of Dissipative Differential Equations Subject to Random Impulses Journal of Differential Equations; Vol. 155; No. 2; https://doi.org/10.1006/jdeq.1998.3594

• Gonzalez, O.; Higham, D. J. et al. (1999) Qualitative properties of modified equations IMA Journal of Numerical Analysis; Vol. 19; No. 2; https://doi.org/10.1093/imanum/19.2.169

• Lamba, H.; Stuart, A. M. (1998) Convergence results for the MATLAB ODE23 routine BIT Numerical Mathematics; Vol. 38; No. 4; https://doi.org/10.1007/BF02510413

• Gander, Martin J.; Stuart, Andrew M. (1998) Space-Time Continuous Analysis of Waveform Relaxation for the Heat Equation SIAM Journal on Scientific Computing; Vol. 19; No. 6; https://doi.org/10.1137/S1064827596305337

• Budd, C. J.; Koomullil, G. P. et al. (1998) On the Solution of Convection-Diffusion Boundary Value Problems Using Equidistributed Grids SIAM Journal on Scientific Computing; Vol. 20; No. 2; https://doi.org/10.1137/S1064827595280454

• Jones, Don A.; Stuart, Andrew M. et al. (1998) Persistence of Invariant Sets for Dissipative Evolution Equations Journal of Mathematical Analysis and Applictions; Vol. 219; No. 2; https://doi.org/10.1006/jmaa.1997.5847

• Higham, D. J.; Stuart, A. M. (1998) Analysis of the dynamics of local error control via a piecewise continuous residual BIT Numerical Mathematics; Vol. 38; No. 1; https://doi.org/10.1007/BF02510916

• Bjørhus, Morten; Stuart, Andrew M. (1997) Waveform relaxation as a dynamical system Mathematics of Computation; Vol. 66; No. 219; https://doi.org/10.1090/S0025-5718-97-00847-8

• Stuart, A. M. (1997) Probabilistic and deterministic convergence proofs for software for initial value problems Numerical Algorithms; Vol. 14; No. 1/3; https://doi.org/10.1023/A:1019169114976

• Elliott, C. M.; Stuart, A. M. (1996) Viscous Cahn–Hilliard Equation II. Analysis Journal of Differential Equations; Vol. 128; No. 2; https://doi.org/10.1006/jdeq.1996.0101

• Jones, D. A.; Stuart, A. M. (1995) Attractive Invariant Manifolds under Approximation. Inertial Manifolds Journal of Differential Equations; Vol. 123; No. 2; https://doi.org/10.1006/jdeq.1995.1174

• Stuart, A. M.; Humphries, A. R. (1995) The Essential Stability of Local Error Control for Dynamical Systems SIAM Journal on Numerical Analysis; Vol. 32; No. 6; https://doi.org/10.1137/0732087

• Estep, Donald J.; Stuart, Andrew M. (1995) The rate of error growth in Hamiltonian-conserving integrators Zeitschrift für Angewandte Mathematik und Physik; Vol. 46; No. 3; https://doi.org/10.1007/BF01003559

• Lord, Gabriel J.; Stuart, Andrew M. (1995) Discrete Gevrey regularity attractors and uppers–semicontinuity for a finite difference approximation to the Ginzburg–Landau equation Numerical Functional Analysis and Optimization; Vol. 16; No. 7-8; https://doi.org/10.1080/01630569508816658

• Bai, F.; Elliott, C. M. et al. (1995) The viscous Cahn-Hilliard equation. I. Computations Nonlinearity; Vol. 8; No. 2; https://doi.org/10.1088/0951-7715/8/2/002

• Bai, Fengshan; Spence, Alastair et al. (1994) Numerical computations of coarsening in the one-dimensional Cahn-Hilliard model of phase separation Physica D; Vol. 78; No. 3-4; https://doi.org/10.1016/0167-2789(94)90112-0

• Humphries, A. R.; Stuart, A. M. (1994) Runge–Kutta Methods for Dissipative and Gradient Dynamical Systems SIAM Journal on Numerical Analysis; Vol. 31; No. 5; https://doi.org/10.1137/0731075

• Budd, C. J.; Dold, J. W. et al. (1994) Blow-up in a System of Partial Differential Equations with Conserved First Integral. Part II: Problems with Convection SIAM Journal on Applied Mathematics; Vol. 54; No. 3; https://doi.org/10.1137/S0036139992232131

• Stuart, A. M.; Humphries, A. R. (1994) Model Problems in Numerical Stability Theory for Initial Value Problems SIAM Review; Vol. 36; No. 2; https://doi.org/10.1137/1036054

• Stuart, Andrew M. (1994) Numerical analysis of dynamical systems Acta Numerica; Vol. 3; https://doi.org/10.1017/S0962492900002488

• Elliott, C. M.; Stuart, A. M. (1993) The Global Dynamics of Discrete Semilinear Parabolic Equations SIAM Journal on Numerical Analysis; Vol. 30; No. 6; In: Dynamics of Numerics and Numerics of Dynamics, July 1990, Bristol, UK https://doi.org/10.1137/0730084

• Chaplain, M. A. J.; Stuart, A. M. (1993) A model mechanism for the chemotactic response of endothelial cells to tumour angiogenesis factor Mathematical Medicine and Biology; Vol. 10; No. 3; In: Sixth IMA Conference on the Mathematical Theory of the Dynamics of Biological Systems, 1-3 July 1992, Oxford, UK https://doi.org/10.1093/imammb/10.3.149

• Budd, Chris; Dold, Bill et al. (1993) Blowup in a Partial Differential Equation with Conserved First Integral SIAM Journal on Applied Mathematics; Vol. 53; No. 3; https://doi.org/10.1137/0153036

• Bai, Fengshan; Spence, Alastair et al. (1993) The Numerical Computation of Heteroclinic Connections in Systems of Gradient Partial Differential Equations SIAM Journal on Applied Mathematics; Vol. 53; No. 3; https://doi.org/10.1137/0153037

• Iserles, A.; Stuart, A. M. (1992) Unified approach to spurious solutions introduced by time discretization Part II: BDF-like methods IMA Journal of Numerical Analysis; Vol. 12; No. 4; https://doi.org/10.1093/imanum/12.4.487

• Griffiths, D. F.; Stuart, A. M. et al. (1992) Numerical Wave Propagation in an Advection Equation with a Nonlinear Source Term SIAM Journal on Numerical Analysis; Vol. 29; No. 5; https://doi.org/10.1137/0729074

• Sanz-Serna, J. M.; Stuart, A. M. (1992) A note on uniform in time error estimates for approximations to reaction-diffusion equations IMA Journal of Numerical Analysis; Vol. 12; No. 3; https://doi.org/10.1093/imanum/12.3.457

• Iserles, A.; Peplow, A. T. et al. (1991) A Unified Approach to Spurious Solutions Introduced by Time Discretisation. Part I: Basic Theory SIAM Journal on Numerical Analysis; Vol. 28; No. 6; https://doi.org/10.1137/0728086

• Stuart, A. M.; Peplow, A. T. (1991) The Dynamics of the Theta Method SIAM Journal on Scientific and Statistical Computing; Vol. 12; No. 6; https://doi.org/10.1137/0912074

• Chaplain, M. A. J.; Stuart, A. M. (1991) A Mathematical Model for the Diffusion of Tumour Angiogenesis Factor into the Surrounding Host Tissue Mathematical Medicine and Biology; Vol. 8; No. 3; https://doi.org/10.1093/imammb/8.3.191

• Stuart, Andrew M. (1991) Singular Limits in Free Boundary Problems Rocky Mountain Journal of Mathematics; Vol. 21; No. 2; https://doi.org/10.1216/rmjm/1181072969

• Stuart, A. M.; Floater, M. S. (1990) On the computation of blow-up European Journal of Applied Mathematics; Vol. 1; No. 01; https://doi.org/10.1017/S095679250000005X

• Stuart, Andrew (1989) Linear Instability Implies Spurious Periodic Solutions IMA Journal of Numerical Analysis; Vol. 9; No. 4; https://doi.org/10.1093/imanum/9.4.465

• Stuart, Andrew (1989) Nonlinear Instability in Dissipative Finite Difference Schemes SIAM Review; Vol. 31; No. 2; https://doi.org/10.1137/1031048

• Norbury, J.; Stuart, A. M. (1989) A Model for Porous-Medium Combustion Quarterly Journal of Mechanics and Applied Mathematics; Vol. 42; No. 1; https://doi.org/10.1093/qjmam/42.1.159

• Stuart, A. M. (1989) Singular Free Boundary Problems and Local Bifurcation Theory SIAM Journal on Applied Mathematics; Vol. 49; No. 1; https://doi.org/10.1137/0149004

• Stuart, Andrew (1989) A Note on High/Low-Wave-Number Interactions in Spatially Discrete Parabolic Equations IMA Journal of Applied Mathematics; Vol. 42; No. 1; https://doi.org/10.1093/imamat/42.1.27

• Stuart, Andrew (1988) Similarity Solutions of a Heat Equation with Nonlinearly Varying Heat Capacity IMA Journal of Applied Mathematics; Vol. 40; No. 3; https://doi.org/10.1093/imamat/40.3.217

• Norbury, J.; Stuart, A. M. (1988) Travelling Combustion Waves in a Porous Medium. Part II—Stability SIAM Journal on Applied Mathematics; Vol. 48; No. 2; https://doi.org/10.1137/0148019

• Norbury, J.; Stuart, A. M. (1988) Travelling Combustion Waves in a Porous Medium. Part I—Existence SIAM Journal on Applied Mathematics; Vol. 48; No. 1; https://doi.org/10.1137/0148007

• Norbury, J.; Stuart, A. M. (1987) Parabolic Free Boundary Problems Arising in Porous Medium Combustion IMA Journal of Applied Mathematics; Vol. 39; No. 3; https://doi.org/10.1093/imamat/39.3.241

• Stuart, A. M. (1987) Existence of Solutions of a Two-Point Free-Boundary Problem Arising in the Theory of Porous Medium Combustion IMA Journal of Applied Mathematics; Vol. 38; No. 1; https://doi.org/10.1093/imamat/38.1.23

• Norbury, J.; Stuart, A. M. (1987) Volterra integral equations and a new Gronwall inequality (Part I: The linear case) Proceedings of the Royal Society of Edinburgh: Section A Mathematics; Vol. 106; No. 3-4; https://doi.org/10.1017/S0308210500018473

• Norbury, J.; Stuart, A. M. (1987) Volterra integral equations and a new Gronwall inequality (Part II: The nonlinear case) Proceedings of the Royal Society of Edinburgh: Section A Mathematics; Vol. 106; No. 3-4; https://doi.org/10.1017/S0308210500018485