Andrew M. Stuart

ORCID

Publication Activity (10 Years)

Years Active: 1989-2024
Publications (10 Years): 84

Top Venues

CoRR

SIAM/ASA J. Uncertain. Quantification

J. Comput. Phys.

Stat. Comput.

Publications

Ömer Deniz Akyildiz, Mark Girolami, Andrew M. Stuart, Arnaud Vadeboncoeur
Efficient Prior Calibration From Indirect Data. CoRR (2024)
Nikola B. Kovachki, Samuel Lanthaler, Andrew M. Stuart
Operator Learning: Algorithms and Analysis. CoRR (2024)
Jinlong Wu, Matthew E. Levine, Tapio Schneider, Andrew M. Stuart
Learning about structural errors in models of complex dynamical systems. J. Comput. Phys. 513 (2024)
Yifan Chen, Daniel Zhengyu Huang, Jiaoyang Huang, Sebastian Reich, Andrew M. Stuart
Efficient, Multimodal, and Derivative-Free Bayesian Inference With Fisher-Rao Gradient Flows. CoRR (2024)
Edoardo Calvello, Nikola B. Kovachki, Matthew E. Levine, Andrew M. Stuart
Continuum Attention for Neural Operators. CoRR (2024)
Jin-Long Wu, Matthew E. Levine, Tapio Schneider, Andrew M. Stuart
Learning About Structural Errors in Models of Complex Dynamical Systems. CoRR (2024)
Nicholas H. Nelsen, Andrew M. Stuart
Operator Learning Using Random Features: A Tool for Scientific Computing. SIAM Rev. 66 (3) (2024)
Yifan Chen, Bamdad Hosseini, Houman Owhadi, Andrew M. Stuart
Gaussian Measures Conditioned on Nonlinear Observations: Consistency, MAP Estimators, and Simulation. CoRR (2024)
Samuel Lanthaler, Andrew M. Stuart, Margaret Trautner
Discretization Error of Fourier Neural Operators. CoRR (2024)
Kaushik Bhattacharya, Nikola B. Kovachki, Aakila Rajan, Andrew M. Stuart, Margaret Trautner
Learning Homogenization for Elliptic Operators. SIAM J. Numer. Anal. 62 (4) (2024)
Kaushik Bhattacharya, Nikola B. Kovachki, Aakila Rajan, Andrew M. Stuart, Margaret Trautner
Learning Homogenization for Elliptic Operators. CoRR (2023)
Tapio Helin, Andrew M. Stuart, Aretha L. Teckentrup, Konstantinos Zygalakis
Introduction To Gaussian Process Regression In Bayesian Inverse Problems, With New ResultsOn Experimental Design For Weighted Error Measures. CoRR (2023)
Samuel Lanthaler, Andrew M. Stuart
The curse of dimensionality in operator learning. CoRR (2023)
Yifan Chen, Daniel Zhengyu Huang, Jiaoyang Huang, Sebastian Reich, Andrew M. Stuart
Sampling via Gradient Flows in the Space of Probability Measures. CoRR (2023)
Nikola B. Kovachki, Zongyi Li, Burigede Liu, Kamyar Azizzadenesheli, Kaushik Bhattacharya, Andrew M. Stuart, Anima Anandkumar
Neural Operator: Learning Maps Between Function Spaces With Applications to PDEs. J. Mach. Learn. Res. 24 (2023)
Yifan Chen, Daniel Zhengyu Huang, Jiaoyang Huang, Sebastian Reich, Andrew M. Stuart
Gradient Flows for Sampling: Mean-Field Models, Gaussian Approximations and Affine Invariance. CoRR (2023)
Maarten V. de Hoop, Nikola B. Kovachki, Nicholas H. Nelsen, Andrew M. Stuart
Convergence Rates for Learning Linear Operators from Noisy Data. SIAM/ASA J. Uncertain. Quantification 11 (2) (2023)
Kaushik Bhattacharya, Burigede Liu, Andrew M. Stuart, Margaret Trautner
Learning Markovian Homogenized Models in Viscoelasticity. Multiscale Model. Simul. 21 (2) (2023)
Pau Batlle, Yifan Chen, Bamdad Hosseini, Houman Owhadi, Andrew M. Stuart
Error Analysis of Kernel/GP Methods for Nonlinear and Parametric PDEs. CoRR (2023)
Anshuman Pradhan, Kyra H. Adams, Venkat Chandrasekaran, Zhen Liu, John T. Reager, Andrew M. Stuart, Michael J. Turmon
Modeling groundwater levels in California's Central Valley by hierarchical Gaussian process and neural network regression. CoRR (2023)
Samuel Lanthaler, Zongyi Li, Andrew M. Stuart
The Nonlocal Neural Operator: Universal Approximation. CoRR (2023)
Assyr Abdulle, Giacomo Garegnani, Grigorios A. Pavliotis, Andrew M. Stuart, Andrea Zanoni
Drift Estimation of Multiscale Diffusions Based on Filtered Data. Found. Comput. Math. 23 (1) (2023)
Oliver R. A. Dunbar, Andrew B. Duncan, Andrew M. Stuart, Marie-Therese Wolfram
Ensemble Inference Methods for Models With Noisy and Expensive Likelihoods. SIAM J. Appl. Dyn. Syst. 21 (2) (2022)
Ziming Liu, Andrew M. Stuart, Yixuan Wang
Second Order Ensemble Langevin Method for Sampling and Inverse Problems. CoRR (2022)
Daniel Zhengyu Huang, Tapio Schneider, Andrew M. Stuart
Iterated Kalman methodology for inverse problems. J. Comput. Phys. 463 (2022)
Kaushik Bhattacharya, Burigede Liu, Andrew M. Stuart, Margaret Trautner
Learning Markovian Homogenized Models in Viscoelasticity. CoRR (2022)
Zongyi Li, Miguel Liu-Schiaffini, Nikola B. Kovachki, Kamyar Azizzadenesheli, Burigede Liu, Kaushik Bhattacharya, Andrew M. Stuart, Anima Anandkumar
Learning Chaotic Dynamics in Dissipative Systems. NeurIPS (2022)
Daniel Zhengyu Huang, Jiaoyang Huang, Sebastian Reich, Andrew M. Stuart
Efficient Derivative-free Bayesian Inference for Large-Scale Inverse Problems. CoRR (2022)
Grigorios A. Pavliotis, Andrew M. Stuart, Urbain Vaes
Derivative-Free Bayesian Inversion Using Multiscale Dynamics. SIAM J. Appl. Dyn. Syst. 21 (1) (2022)
Tapio Schneider, Andrew M. Stuart, Jin-Long Wu
Ensemble Kalman inversion for sparse learning of dynamical systems from time-averaged data. J. Comput. Phys. 470 (2022)
Maarten V. de Hoop, Daniel Zhengyu Huang, Elizabeth Qian, Andrew M. Stuart
The Cost-Accuracy Trade-Off In Operator Learning With Neural Networks. CoRR (2022)
Edoardo Calvello, Sebastian Reich, Andrew M. Stuart
Ensemble Kalman Methods: A Mean Field Perspective. CoRR (2022)
Daniel Z. Huang, Tapio Schneider, Andrew M. Stuart
Unscented Kalman Inversion. CoRR (2021)
Yifan Chen, Bamdad Hosseini, Houman Owhadi, Andrew M. Stuart
Solving and Learning Nonlinear PDEs with Gaussian Processes. CoRR (2021)
Zongyi Li, Nikola B. Kovachki, Kamyar Azizzadenesheli, Burigede Liu, Kaushik Bhattacharya, Andrew M. Stuart, Anima Anandkumar
Markov Neural Operators for Learning Chaotic Systems. CoRR (2021)
Yifan Chen, Houman Owhadi, Andrew M. Stuart
Consistency of empirical Bayes and kernel flow for hierarchical parameter estimation. Math. Comput. 90 (332) (2021)
Emmet Cleary, Alfredo Garbuno-Inigo, Shiwei Lan, Tapio Schneider, Andrew M. Stuart
Calibrate, emulate, sample. J. Comput. Phys. 424 (2021)
J. A. Carrillo, Franca Hoffmann, Andrew M. Stuart, Urbain Vaes
Consensus Based Sampling. CoRR (2021)
Matthew E. Levine, Andrew M. Stuart
A Framework for Machine Learning of Model Error in Dynamical Systems. CoRR (2021)
Andrew B. Duncan, Andrew M. Stuart, Marie-Therese Wolfram
Ensemble Inference Methods for Models With Noisy and Expensive Likelihoods. CoRR (2021)
Zongyi Li, Nikola Borislavov Kovachki, Kamyar Azizzadenesheli, Burigede Liu, Kaushik Bhattacharya, Andrew M. Stuart, Anima Anandkumar
Fourier Neural Operator for Parametric Partial Differential Equations. ICLR (2021)
Maarten V. de Hoop, Nikola B. Kovachki, Nicholas H. Nelsen, Andrew M. Stuart
Convergence Rates for Learning Linear Operators from Noisy Data. CoRR (2021)
Nikola B. Kovachki, Andrew M. Stuart
Continuous Time Analysis of Momentum Methods. J. Mach. Learn. Res. 22 (2021)
Yifan Chen, Bamdad Hosseini, Houman Owhadi, Andrew M. Stuart
Solving and learning nonlinear PDEs with Gaussian processes. J. Comput. Phys. 447 (2021)
Nicholas H. Nelsen, Andrew M. Stuart
The Random Feature Model for Input-Output Maps between Banach Spaces. SIAM J. Sci. Comput. 43 (5) (2021)
Nikola B. Kovachki, Zongyi Li, Burigede Liu, Kamyar Azizzadenesheli, Kaushik Bhattacharya, Andrew M. Stuart, Anima Anandkumar
Neural Operator: Learning Maps Between Function Spaces. CoRR (2021)
Oliver R. A. Dunbar, Matthew M. Dunlop, Charles M. Elliott, Viet Ha Hoang, Andrew M. Stuart
Reconciling Bayesian and Perimeter Regularization for Binary Inversion. SIAM J. Sci. Comput. 42 (4) (2020)
Neil K. Chada, Andrew M. Stuart, Xin T. Tong
Tikhonov Regularization within Ensemble Kalman Inversion. SIAM J. Numer. Anal. 58 (2) (2020)
Kaushik Bhattacharya, Bamdad Hosseini, Nikola B. Kovachki, Andrew M. Stuart
Model Reduction and Neural Networks for Parametric PDEs. CoRR (2020)
Assyr Abdulle, Giacomo Garegnani, Grigorios A. Pavliotis, Andrew M. Stuart, Andrea Zanoni
Drift Estimation of Multiscale Diffusions Based on Filtered Data. CoRR (2020)
Zongyi Li, Nikola B. Kovachki, Kamyar Azizzadenesheli, Burigede Liu, Andrew M. Stuart, Kaushik Bhattacharya, Anima Anandkumar
Multipole Graph Neural Operator for Parametric Partial Differential Equations. NeurIPS (2020)
Yifan Chen, Houman Owhadi, Andrew M. Stuart
Consistency of Empirical Bayes And Kernel Flow For Hierarchical Parameter Estimation. CoRR (2020)
Nicholas H. Nelsen, Andrew M. Stuart
The Random Feature Model for Input-Output Maps between Banach Spaces. CoRR (2020)
Zongyi Li, Nikola B. Kovachki, Kamyar Azizzadenesheli, Burigede Liu, Kaushik Bhattacharya, Andrew M. Stuart, Anima Anandkumar
Neural Operator: Graph Kernel Network for Partial Differential Equations. CoRR (2020)
David J. Albers, Melike Sirlanci Tuysuzoglu, Matthew E. Levine, Caroline Der Nigoghossian, Andrew M. Stuart, Jan Claassen, Bruce J. Gluckman, George Hripcsak
Lessons learned from assimilating knowledge into machine learning to forecast and control glucose in a critical care setting. AMIA (2020)
Franca Hoffmann, Bamdad Hosseini, Zhi Ren, Andrew M. Stuart
Consistency of Semi-Supervised Learning Algorithms on Graphs: Probit and One-Hot Methods. J. Mach. Learn. Res. 21 (2020)
Zongyi Li, Nikola B. Kovachki, Kamyar Azizzadenesheli, Burigede Liu, Kaushik Bhattacharya, Andrew M. Stuart, Anima Anandkumar
Fourier Neural Operator for Parametric Partial Differential Equations. CoRR (2020)
Andrew M. Stuart, Marie-Therese Wolfram
Inverse Optimal Transport. SIAM J. Appl. Math. 80 (1) (2020)
Zongyi Li, Nikola B. Kovachki, Kamyar Azizzadenesheli, Burigede Liu, Kaushik Bhattacharya, Andrew M. Stuart, Anima Anandkumar
Multipole Graph Neural Operator for Parametric Partial Differential Equations. CoRR (2020)
Kit Newton, Qin Li, Andrew M. Stuart
Diffusive Optical Tomography in the Bayesian Framework. Multiscale Model. Simul. 18 (2) (2020)
Alfredo Garbuno-Inigo, Franca Hoffmann, Wuchen Li, Andrew M. Stuart
Interacting Langevin Diffusions: Gradient Structure and Ensemble Kalman Sampler. SIAM J. Appl. Dyn. Syst. 19 (1) (2020)
Andrea L. Bertozzi, Bamdad Hosseini, Hao Li, Kevin Miller, Andrew M. Stuart
Posterior Consistency of Semi-Supervised Regression on Graphs. CoRR (2020)
Nikola B. Kovachki, Andrew M. Stuart
Analysis Of Momentum Methods. CoRR (2019)
Franca Hoffmann, Bamdad Hosseini, Zhi Ren, Andrew M. Stuart
Consistency of semi-supervised learning algorithms on graphs: Probit and one-hot methods. CoRR (2019)
Yiling Qiao, Chang Shi, Chenjian Wang, Hao Li, Matt Haberland, Xiyang Luo, Andrew M. Stuart, Andrea L. Bertozzi
Uncertainty quantification for semi-supervised multi-class classification in image processing and ego-motion analysis of body-worn videos. Image Processing: Algorithms and Systems (2019)
Han Cheng Lie, Andrew M. Stuart, Timothy John Sullivan
Strong convergence rates of probabilistic integrators for ordinary differential equations. Stat. Comput. 29 (6) (2019)
Susana N. Gomes, Andrew M. Stuart, Marie-Therese Wolfram
Parameter Estimation for Macroscopic Pedestrian Dynamics Models from Microscopic Data. SIAM J. Appl. Math. 79 (4) (2019)
David J. Albers, Matthew E. Levine, Andrew M. Stuart, Lena Mamykina, Bruce J. Gluckman, George Hripcsak
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. 25 (10) (2018)
Matthew M. Dunlop, Dejan Slepcev, Andrew M. Stuart, Matthew Thorpe
Large Data and Zero Noise Limits of Graph-Based Semi-Supervised Learning Algorithms. CoRR (2018)
Nikola B. Kovachki, Andrew M. Stuart
Ensemble Kalman Inversion: A Derivative-Free Technique For Machine Learning Tasks. CoRR (2018)
Andrea L. Bertozzi, Xiyang Luo, Andrew M. Stuart, Konstantinos C. Zygalakis
Uncertainty Quantification in Graph-Based Classification of High Dimensional Data. SIAM/ASA J. Uncertain. Quantification 6 (2) (2018)
David J. Albers, Matthew E. Levine, Andrew M. Stuart, Jan Claassen, Bruce J. Gluckman, George Hripcsak
Using mechanistic machine learning to forecast glucose and infer physiologic phenotypes in the ICU: what is possible and what are the challenges. AMIA (2018)
Andrew M. Stuart, Aretha L. Teckentrup
Posterior consistency for Gaussian process approximations of Bayesian posterior distributions. Math. Comput. 87 (310) (2018)
Matthew M. Dunlop, Mark A. Girolami, Andrew M. Stuart, Aretha L. Teckentrup
How Deep Are Deep Gaussian Processes? J. Mach. Learn. Res. 19 (2018)
Robert Scheichl, Andrew M. Stuart, Aretha L. Teckentrup
Quasi-Monte Carlo and Multilevel Monte Carlo Methods for Computing Posterior Expectations in Elliptic Inverse Problems. SIAM/ASA J. Uncertain. Quantification 5 (1) (2017)
David J. Albers, Matthew E. Levine, Andrew M. Stuart, Bruce J. Gluckman, George Hripcsak
Why predicting postprandial glucose using self-monitoring data is difficult. AMIA (2017)
Matthew M. Dunlop, Marco A. Iglesias, Andrew M. Stuart
Hierarchical Bayesian level set inversion. Stat. Comput. 27 (6) (2017)
Andrea L. Bertozzi, Xiyang Luo, Andrew M. Stuart, Konstantinos C. Zygalakis
Uncertainty Quantification in the Classification of High Dimensional Data. CoRR (2017)
Yulong Lu, Andrew M. Stuart, Hendrik Weber
. SIAM/ASA J. Uncertain. Quantification 5 (1) (2017)
Claudia Schillings, Andrew M. Stuart
Analysis of the Ensemble Kalman Filter for Inverse Problems. SIAM J. Numer. Anal. 55 (3) (2017)
Alexandros Beskos, Mark A. Girolami, Shiwei Lan, Patrick E. Farrell, Andrew M. Stuart
Geometric MCMC for infinite-dimensional inverse problems. J. Comput. Phys. 335 (2017)
Patrick R. Conrad, Mark A. Girolami, Simo Särkkä, Andrew M. Stuart, Konstantinos Zygalakis
Statistical analysis of differential equations: introducing probability measures on numerical solutions. Stat. Comput. 27 (4) (2017)
Yulong Lu, Andrew M. Stuart, Hendrik Weber
Gaussian Approximations for Transition Paths in Brownian Dynamics. SIAM J. Math. Anal. 49 (4) (2017)
David J. Albers, Matthew E. Levine, Andrew M. Stuart, George Hripcsak, Lena Mamykina
Using data assimilation to forecast post-meal glucose for patients with type 2 diabetes. AMIA (2016)
Francis J. Pinski, Gideon Simpson, Andrew M. Stuart, Hendrik Weber
Algorithms for Kullback-Leibler Approximation of Probability Measures in Infinite Dimensions. SIAM J. Sci. Comput. 37 (6) (2015)
Francis J. Pinski, Gideon Simpson, Andrew M. Stuart, Hendrik Weber
Kullback-Leibler Approximation for Probability Measures on Infinite Dimensional Spaces. SIAM J. Math. Anal. 47 (6) (2015)
Daniel Sanz-Alonso, Andrew M. Stuart
Long-Time Asymptotics of the Filtering Distribution for Partially Observed Chaotic Dynamical Systems. SIAM/ASA J. Uncertain. Quantification 3 (1) (2015)
Alexandros Beskos, Ajay Jasra, Ege A. Muzaffer, Andrew M. Stuart
Sequential Monte Carlo methods for Bayesian elliptic inverse problems. Stat. Comput. 25 (4) (2015)
Andrew B. Duncan, Charles M. Elliott, Grigorios A. Pavliotis, Andrew M. Stuart
A Multiscale Analysis of Diffusions on Rapidly Varying Surfaces. J. Nonlinear Sci. 25 (2) (2015)
Sergios Agapiou, Johnathan M. Bardsley, Omiros Papaspiliopoulos, Andrew M. Stuart
Analysis of the Gibbs Sampler for Hierarchical Inverse Problems. SIAM/ASA J. Uncertain. Quantification 2 (1) (2014)
Kody J. H. Law, Andrew M. Stuart
Evaluating Data Assimilation Algorithms CoRR (2011)
M. Dashti, Andrew M. Stuart
Uncertainty Quantification and Weak Approximation of an Elliptic Inverse Problem. SIAM J. Numer. Anal. 49 (6) (2011)
Jonathan C. Mattingly, Andrew M. Stuart, Michael V. Tretyakov
Convergence of Numerical Time-Averaging and Stationary Measures via Poisson Equations. SIAM J. Numer. Anal. 48 (2) (2010)
Andrew M. Stuart
Inverse problems: A Bayesian perspective. Acta Numer. 19 (2010)
S. L. Cotter, M. Dashti, Andrew M. Stuart
Approximation of Bayesian Inverse Problems for PDEs. SIAM J. Numer. Anal. 48 (1) (2010)
Grigorios A. Pavliotis, Andrew M. Stuart, Konstantinos C. Zygalakis
Calculating effective diffusivities in the limit of vanishing molecular diffusion. J. Comput. Phys. 228 (4) (2009)
Yvo Pokern, Andrew M. Stuart, Eric Vanden-Eijnden
Remarks on Drift Estimation for Diffusion Processes. Multiscale Model. Simul. 8 (1) (2009)
Dwight Barkley, Ioannis G. Kevrekidis, Andrew M. Stuart
The Moment Map: Nonlinear Dynamics of Density Evolution via a Few Moments. SIAM J. Appl. Dyn. Syst. 5 (3) (2006)
Grigorios A. Pavliotis, Andrew M. Stuart
Analysis of White Noise Limits for Stochastic Systems with Two Fast Relaxation Times. Multiscale Model. Simul. 4 (1) (2005)
Desmond J. Higham, Xuerong Mao, Andrew M. Stuart
Exponential Mean-Square Stability of Numerical Solutions to Stochastic Differential Equations. LMS J. Comput. Math. 6 (2003)

Andrew M. Stuart.css-eqlu8i{font-size:small;padding-left:4px;}

Publication Activity (10 Years)

Top Topics

Top Venues

Andrew M. Stuart