Andrew M. Stuart | Academic Influence

Q: What Schools Are Affiliated With Andrew M. Stuart

Andrew M. Stuart is affiliated with the following schools: University of Warwick, California Institute of Technology

Why Is Andrew M. Stuart Influential?

Andrew M. Stuart's Published Works

Number of citations in a given year to any of this author's works

Total number of citations to an author for the works they published in a given year. This highlights publication of the most important work(s) by the author

Published Works

Inverse problems: A Bayesian perspective (2010) (1410)
Multiscale Methods: Averaging and Homogenization (2008) (885)
Strong Convergence of Euler-Type Methods for Nonlinear Stochastic Differential Equations (2002) (593)
Ergodicity for SDEs and approximations: locally Lipschitz vector fields and degenerate noise (2002) (578)
MCMC Methods for Functions: ModifyingOld Algorithms to Make Them Faster (2012) (526)
The Bayesian Approach to Inverse Problems (2013) (498)
Dynamical Systems And Numerical Analysis (1996) (484)
A First Course in Continuum Mechanics (2008) (413)
Extracting macroscopic dynamics: model problems and algorithms (2004) (388)
Optimal tuning of the hybrid Monte Carlo algorithm (2010) (288)
Ensemble Kalman methods for inverse problems (2012) (254)
The global dynamics of discrete semilinear parabolic equations (1993) (245)
Data Assimilation: A Mathematical Introduction (2015) (236)
Space-Time Continuous Analysis of Waveform Relaxation for the Heat Equation (1998) (190)
MCMC methods for diffusion bridges (2008) (184)
Model Reduction and Neural Networks for Parametric PDEs (2020) (171)
Analysis of the Ensemble Kalman Filter for Inverse Problems (2016) (164)
Spectral gaps for a Metropolis–Hastings algorithm in infinite dimensions (2011) (163)
Bayesian inverse problems for functions and applications to fluid mechanics (2009) (163)
Convergence of Numerical Time-Averaging and Stationary Measures via Poisson Equations (2009) (158)
Exponential mean square stability of numerical solutions to stochastic differential equations (2003) (152)
MAP estimators and their consistency in Bayesian nonparametric inverse problems (2013) (146)
Sampling the posterior: An approach to non-Gaussian data assimilation (2007) (135)
Hybrid Monte Carlo on Hilbert spaces (2011) (130)
ANALYSIS OF SPDES ARISING IN PATH SAMPLING PART II: THE NONLINEAR CASE (2006) (129)
Interacting Langevin Diffusions: Gradient Structure and Ensemble Kalman Sampler (2019) (119)
Runge-Kutta methods for dissipative and gradient dynamical systems (1994) (117)
Parameter Estimation for Multiscale Diffusions (2006) (115)
Approximation of Bayesian Inverse Problems for PDEs (2009) (114)
Sparse deterministic approximation of Bayesian inverse problems (2011) (112)
Well-posedness and accuracy of the ensemble Kalman filter in discrete and continuous time (2013) (112)
Importance Sampling: Intrinsic Dimension and Computational Cost (2015) (111)
Viscous Cahn–Hilliard Equation II. Analysis (1996) (109)
Optimal scalings for local Metropolis--Hastings chains on nonproduct targets in high dimensions (2009) (108)
Evaluating Data Assimilation Algorithms (2011) (107)
Geometric MCMC for infinite-dimensional inverse problems (2016) (106)
Diffusion limits of the random walk metropolis algorithm in high dimensions (2010) (103)
Conditional Path Sampling of SDEs and the Langevin MCMC Method (2004) (101)
Optimal Scaling and Diffusion Limits for the Langevin Algorithm in High Dimensions (2011) (100)
How Deep Are Deep Gaussian Processes? (2017) (99)
Itô versus Stratonovich white-noise limits for systems with inertia and colored multiplicative noise. (2004) (99)
Complexity analysis of accelerated MCMC methods for Bayesian inversion (2012) (98)
Besov priors for Bayesian inverse problems (2011) (97)
Posterior consistency for Gaussian process approximations of Bayesian posterior distributions (2016) (96)
Analysis of SPDEs arising in path sampling. Part I: The Gaussian case (2005) (93)
Uncertainty Quantification and Weak Approximation of an Elliptic Inverse Problem (2011) (91)
Model Problems in Numerical Stability Theory for Initial Value Problems (1994) (89)
The Random Feature Model for Input-Output Maps between Banach Spaces (2020) (86)
Ensemble Kalman inversion: a derivative-free technique for machine learning tasks (2018) (86)
Hierarchical Bayesian level set inversion (2016) (84)
Algorithms for particle-field simulations with collisions (2001) (82)
A note on diffusion limits of chaotic skew-product flows (2011) (81)
Data assimilation: Mathematical and statistical perspectives (2008) (81)
Posterior Contraction Rates for the Bayesian Approach to Linear Ill-Posed Inverse Problems (2012) (80)
The viscous Cahn-Hilliard equation. I. Computations (1995) (79)
Statistical analysis of differential equations: introducing probability measures on numerical solutions (2016) (78)
An adaptive Euler-Maruyama scheme for SDEs: convergence and stability (2006) (76)
Nonparametric estimation of diffusions: a differential equations approach (2012) (74)
Blowup in a Partial Differential Equation with Conserved First Integral (1993) (72)
Well-posed Bayesian geometric inverse problems arising in subsurface flow (2014) (71)
Numerical wave propagation in an advection equation with a nonlinear source term (1992) (70)
Geometric Ergodicity of Some Hypo-Elliptic Diffusions for Particle Motions (2002) (69)
Sequential Monte Carlo methods for Bayesian elliptic inverse problems (2014) (69)
Accuracy and stability of the continuous-time 3DVAR filter for the Navier–Stokes equation (2012) (67)
Evaluation of Gaussian approximations for data assimilation in reservoir models (2012) (67)
Convergence analysis of ensemble Kalman inversion: the linear, noisy case (2017) (66)
A Bayesian Level Set Method for Geometric Inverse Problems (2015) (66)
On the computation of blow-up (1990) (63)
Parameter estimation for partially observed hypoelliptic diffusions (2007) (62)
Maximum likelihood drift estimation for multiscale diffusions (2008) (61)
A model for preferential concentration (2002) (60)
A Function Space HMC Algorithm With Second Order Langevin Diffusion Limit (2013) (60)
Random‐weight particle filtering of continuous time processes (2010) (60)
Parameterizations for ensemble Kalman inversion (2017) (57)
A unified approach to spurious solutions introduced by time discretization. Part I: basic theory (1991) (56)
A Perturbation Theory for Ergodic Markov Chains and Application to Numerical Approximations (2000) (56)
Calibrate, emulate, sample (2020) (55)
Kullback-Leibler Approximation for Probability Measures on Infinite Dimensional Spaces (2013) (55)
Tikhonov Regularization within Ensemble Kalman Inversion (2019) (54)
Posterior consistency via precision operators for Bayesian nonparametric drift estimation in SDEs (2012) (54)
The Bayesian Formulation of EIT: Analysis and Algorithms (2015) (53)
Analysis of the Gibbs Sampler for Hierarchical Inverse Problems (2013) (53)
Quasi-Monte Carlo and Multilevel Monte Carlo Methods for Computing Posterior Expectations in Elliptic Inverse Problems (2016) (53)
Periodic homogenization for inertial particles (2005) (53)
Analysis of the 3DVAR filter for the partially observed Lorenz'63 model (2012) (52)
Solving and Learning Nonlinear PDEs with Gaussian Processes (2021) (52)
A Bayesian approach to Lagrangian data assimilation (2008) (52)
MCMC methods for sampling function space (2009) (51)
Algorithms for Kullback-Leibler Approximation of Probability Measures in Infinite Dimensions (2014) (50)
Large Data and Zero Noise Limits of Graph-Based Semi-Supervised Learning Algorithms (2018) (49)
Uncertainty Quantification in Graph-Based Classification of High Dimensional Data (2017) (47)
Iterative updating of model error for Bayesian inversion (2017) (45)
LONG-TERM BEHAVIOUR OF LARGE MECHANICAL SYSTEMS WITH RANDOM INITIAL DATA (2002) (44)
Ergodicity of Dissipative Differential Equations Subject to Random Impulses (1999) (42)
The Numerical Computation of Heteroclinic Connections in Systems of Gradient Partial Differential Equations (1993) (40)
Ensemble Kalman methods with constraints (2019) (39)
Nonlinear Instability in Dissipative Finite Difference Schemes (1989) (39)
A model for porous-medium combustion (1989) (39)
Signal processing problems on function space: Bayesian formulation, stochastic PDEs and effective MCMC methods (2011) (39)
Numerical analysis of dynamical systems (1994) (38)
Accuracy and stability of filters for dissipative PDEs (2012) (37)
White Noise Limits for Inertial Particles in a Random Field (2003) (37)
Extracting macroscopic stochastic dynamics: Model problems (2003) (37)
SPDE limits of the random walk Metropolis algorithm in high dimensions (2009) (35)
Analysis and Experiments for a Computational Model of a Heat Bath (1999) (35)
Bayesian posterior contraction rates for linear severely ill-posed inverse problems (2012) (35)
Transition paths in molecules at finite temperature (2010) (32)
The Dynamics of the Theta Method (1991) (32)
Analysis of White Noise Limits for Stochastic Systems with Two Fast Relaxation Times (2005) (31)
Importance Sampling: Computational Complexity and Intrinsic Dimension (2015) (31)
Long-Time Asymptotics of the Filtering Distribution for Partially Observed Chaotic Dynamical Systems (2014) (30)
Mechanistic machine learning: how data assimilation leverages physiologic knowledge using Bayesian inference to forecast the future, infer the present, and phenotype (2018) (30)
Calculating effective diffusivities in the limit of vanishing molecular diffusion (2009) (29)
Volterra integral equations and a new Gronwall inequality (Part II: The nonlinear case) (1987) (29)
Parameter Estimation for Macroscopic Pedestrian Dynamics Models from Microscopic Data (2018) (28)
Calibration and Uncertainty Quantification of Convective Parameters in an Idealized GCM (2020) (28)
Attractive Invariant Manifolds under Approximation. Inertial Manifolds (1995) (28)
Variational data assimilation using targetted random walks (2012) (28)
Blow-up in a System of Partial Differential Equations with Conserved First Integral. Part II: Problems with Convection (1994) (28)
The rate of error growth in Hamiltonian-conserving integrators (1995) (28)
Perturbation Theory for Infinite Dimensional Dynamical Systems (1995) (27)
The Moment Map: Nonlinear Dynamics of Density Evolution via a Few Moments (2005) (27)
Sampling conditioned diffusions (2009) (26)
Strong convergence rates of probabilistic integrators for ordinary differential equations (2017) (26)
Travelling combustion waves in a porous medium. Part 1—existence (1988) (26)
Probability Theory and Stochastic Processes (2008) (25)
A note on uniform in time error estimates for approximations to reaction-diffusion equations (1992) (25)
An MCMC method for diffusion bridges (2008) (24)
The essential stability of local error control for dynamical systems (1995) (24)
Analysis of the dynamics of local error control via a piecewise continuous residual (1998) (23)
Computational Complexity of Metropolis-Hastings Methods in High Dimensions (2009) (22)
The dynamical behavior of the discontinuous Galerkin method and related difference schemes (2001) (22)
Homogenization for inertial particles in a random flow (2007) (22)
A Framework for Machine Learning of Model Error in Dynamical Systems (2021) (22)
Inverse Problems and Data Assimilation. (2018) (22)
Gaussian Approximations for Probability Measures on Rd (2017) (21)
Filter accuracy for the Lorenz 96 model: Fixed versus adaptive observation operators (2016) (20)
Consensus‐based sampling (2021) (20)
Iterated Kalman methodology for inverse problems (2021) (20)
Sampling conditioned hypoelliptic diffusions (2009) (20)
Linear Instability Implies Spurious Periodic Solutions (1989) (20)
Probability Measures for Numerical Solutions of Differential Equations (2015) (20)
Remarks on Drift Estimation for Diffusion Processes (2009) (20)
Numerical computations of coarsening in the one-dimensional Cahn-Hilliard model of phase separation (1994) (19)
Convergence Rates for Learning Linear Operators from Noisy Data (2021) (19)
Persistence of Invariant Sets for Dissipative Evolution Equations (1998) (19)
Multiscale modelling and inverse problems (2010) (19)
POSTERIOR CONSISTENCY OF THE BAYESIAN APPROACH TO LINEAR ILL-POSED INVERSE PROBLEMS (2012) (19)
Consistency of Empirical Bayes And Kernel Flow For Hierarchical Parameter Estimation (2020) (18)
Stiff Oscillatory Systems, Delta Jumps and White Noise (2001) (18)
Noisy gradient flow from a random walk in Hilbert space (2011) (18)
Learning stochastic closures using ensemble Kalman inversion (2020) (18)
Consistency of semi-supervised learning algorithms on graphs: Probit and one-hot methods (2019) (18)
Uncertainty Quantification in the Classification of High Dimensional Data (2017) (18)
The Cost-Accuracy Trade-Off In Operator Learning With Neural Networks (2022) (18)
Dimension-Robust MCMC in Bayesian Inverse Problems (2018) (18)
INERTIAL PARTICLES IN A RANDOM FIELD (2002) (17)
Gaussian Approximations for Transition Paths in Brownian Dynamics (2016) (17)
Ensemble Inference Methods for Models With Noisy and Expensive Likelihoods (2021) (17)
MAP estimators for piecewise continuous inversion (2015) (17)
Continuous Time Analysis of Momentum Methods (2019) (17)
Unified approach to spurious solutions introduced by time discretization Part II: BDF-like methods (1992) (16)
Fitting SDE models to nonlinear Kac–Zwanzig heat bath models (2004) (16)
Ensemble Kalman inversion for sparse learning of dynamical systems from time-averaged data (2020) (15)
Discrete Gevrey regularity attractors and uppers–semicontinuity for a finite difference approximation to the Ginzburg–Landau equation (1995) (15)
Convergence results for the MATLAB ode23 routine (1998) (15)
Inverse Problems and Uncertainty Quantification (2014) (15)
Sparse MCMC gpc finite element methods for Bayesian inverse problems (2012) (15)
Travelling combustion waves in a porous medium. Part II—Stability (1988) (14)
A Bayesian Approach to Data Assimilation (2005) (14)
Hyperparameter Estimation in Bayesian MAP Estimation: Parameterizations and Consistency (2019) (14)
On the Solution of Convection-Diffusion Boundary Value Problems Using Equidistributed Grids (1998) (14)
Γ-Limit for Transition Paths of Maximal Probability (2011) (13)
GAUSSIAN APPROXIMATIONS FOR PROBABILITY MEASURES ON R (2016) (13)
Weak Error Estimates for Trajectories of SPDEs Under Spectral Galerkin Discretization (2016) (13)
Parabolic Free Boundary Problems Arising in Porous Medium Combustion (1987) (13)
Parameter estimation for multiscale diffusions : an overview (2012) (13)
Waveform relaxation as a dynamical system (1997) (13)
Efficient derivative-free Bayesian inference for large-scale inverse problems (2022) (13)
Spectral analysis of weighted Laplacians arising in data clustering (2019) (13)
Robust MCMC Sampling with Non-Gaussian and Hierarchical Priors in High Dimensions (2018) (13)
Determining white noise forcing from Eulerian observations in the Navier-Stokes equation (2013) (13)
Deterministic and random dynamical systems: theory and numerics (2002) (12)
Diffusion limit for the random walk Metropolis algorithm out of stationarity (2014) (12)
Inverse Optimal Transport (2019) (12)
Statistics From Computations (2001) (12)
Probabilistic and deterministic convergence proofs for software for initial value problems (1997) (12)
Gaussian Approximations of Small Noise Diffusions in Kullback-Leibler Divergence (2016) (11)
Controlling Unpredictability with Observations in the Partially Observed Lorenz '96 Model (2014) (11)
Filter Based Methods For Statistical Linear Inverse Problems (2015) (10)
Non-stationary phase of the MALA algorithm (2016) (10)
Diffusive Optical Tomography in the Bayesian Framework (2019) (10)
Analysis Of Momentum Methods (2019) (10)
Monte Carlo Studies of Effective Diffusivities for Inertial Particles (2006) (10)
Imposing Sparsity Within Ensemble Kalman Inversion (2020) (10)
Ensemble Kalman Methods: A Mean Field Perspective (2022) (10)
Uncertainty quantification for semi-supervised multi-class classification in image processing and ego-motion analysis of body-worn videos (2019) (9)
On the qualitative properties of modified equations (1997) (9)
Optimal Proposal Design for Random Walk Type Metropolis Algorithms with Gaussian Random Field Priors (2011) (9)
Unscented Kalman Inversion (2021) (9)
Approximation of dissipative partial differential equations over long time intervals (1994) (9)
Kalman filtering and smoothing for linear wave equations with model error (2011) (9)
Drift Estimation of Multiscale Diffusions Based on Filtered Data (2020) (9)
Stability of Filters for the Navier-Stokes Equation (2011) (9)
Gradient Structure Of The Ensemble Kalman Flow With Noise (2019) (9)
Underresolved simulations of heat baths (2001) (8)
Derivative-Free Bayesian Inversion Using Multiscale Dynamics (2021) (8)
The Acceptance Probability of the Hybrid Monte Carlo Method in High‐Dimensional Problems (2010) (8)
Optimal scalings of Metropolis-Hastings algorithms for non-product targets in high dimensions (2009) (8)
A Simple Modeling Framework For Prediction In The Human Glucose-Insulin System (2019) (7)
On the random walk metropolis algorithm for Gaussian random field priors and the gradient flow (2011) (7)
Qualitative properties of modified equations (1999) (7)
Gaussian Approximations for Probability Measures on $\mathbf{R}^d$ (2016) (6)
Matrix analysis and algorithms (2009) (6)
Convergence Proofs for Numerical IVP Software (2000) (6)
Ergodicity and Accuracy of Optimal Particle Filters for Bayesian Data Assimilation (2016) (5)
A Multiscale Analysis of Diffusions on Rapidly Varying Surfaces (2013) (5)
Posterior consistency of semi-supervised regression on graphs (2020) (5)
The Global Attractor Under Discretisation (1990) (5)
A Note on High/Low-Wave-Number Interactions in Spatially Discrete Parabolic Equations (1989) (5)
Derivation and analysis of simplified filters (2017) (5)
Uncertainty Quantification in Bayesian Inversion (2014) (4)
Mathematical and Algorithmic Aspects of Data Assimilation in the Geosciences (2017) (4)
Offline and online data assimilation for real-time blood glucose forecasting in type 2 diabetes (2017) (4)
A First Course in Continuum Mechanics: Preface (2008) (4)
MCMC for the Evaluation of Gaussian Approximations to Bayesian Inverse Problems in Groundwater Flow (2012) (3)
Extracting Macroscopic Stochastic Dynamics (2003) (3)
The Mathematics of Porous Medium Combustion (1988) (3)
Ensemble‐Based Experimental Design for Targeting Data Acquisition to Inform Climate Models (2022) (3)
Reconciling Bayesian and Total Variation Methods for Binary Inversion (2017) (3)
Differential Equations Subject to Random Impulses (1997) (3)
A perturbation theory for ergodic properties of Markov chains (1998) (3)
TIKHONOV REGULARIZATION WITHIN ENSEMBLE KALMAN INVERSION\ast (2020) (3)
Approximation of inverse problems (2009) (3)
Reconciling Bayesian and Perimeter Regularization for Binary Inversion (2017) (3)
Existence of Solutions of a Two-Point Free-Boundary Problem Arising in the Theory of Porous Medium Combustion (1987) (3)
Mathematical and Algorithmic Aspects of Atmosphere-Ocean Data Assimilation (2012) (3)
An analysis of local error control for dissipative, contractive and gradient dynamical systems (1992) (3)
Random-weight particle filtering of continuous time (2010) (3)
FAST COMMUNICATION CONDITIONAL PATH SAMPLING OF SDES AND THE LANGEVIN (2004) (2)
The moment map (2020) (2)
Correction to: A note on diusion limits of chaotic skew-product ows (2015) (2)
Filtering systems of coupled stochastic differential equations partially observed at high frequency (2007) (2)
Learning macroscopic internal variables and history dependence from microscopic models (2022) (2)
Numerical Analysis of Evolution Equations. (1997) (2)
The Nonlocal Neural Operator: Universal Approximation (2023) (2)
Continuous Time: Filtering Algorithms (2015) (2)
Data Assimilation and Inverse Problems (2018) (2)
Qualitative properties of modi"ed equations (1999) (2)
Mathematics, Statistics and Data Science (2016) (2)
ON THE SOLUTION OF CONVECTION-DIFFUSION BOUNDARY VALUE PROBLEMS BY GRID ADAPTATION (1995) (2)
Extracting Macroscopic Dynamics : Model Problems & Algorithms (2003) (2)
Bayesian Formulations of Multidimensional Barcode Inversion (2017) (1)
On the Qualitative Properties of Modi (1)
Attractive Invariant Manifolds under Approximation Part I: Inertial Manifolds (1995) (1)
Second Order Ensemble Langevin Method for Sampling and Inverse Problems (2022) (1)
A First Course in Continuum Mechanics: Continuum Mass and Force Concepts (2008) (1)
Discrete Time: Formulation (2015) (1)
Non parametric Bayesian drift estimation for one-dimensional diffusion processes (2009) (1)
Kalman filtering for linear wave equations with model error (2011) (1)
Mathematical foundations of data assimilation problems arising in fluid mechanics (2008) (1)
Singular Limits in Free Boundary Problems (1991) (1)
Homogenization for Elliptic PDEs (2008) (1)
Derivation and Analysis of Simplified Filters for Complex Dynamical Systems (2015) (1)
A free boundary problem arising in smoulder combustion (1992) (1)
OPTIMAL PROPOSALS FOR MCMC METHODS (2010) (1)
Convergence Analysis of the Ensemble Kalman Filter for Inverse Problems: the Noisy Case (2017) (1)
Learning Chaotic Dynamics in Dissipative Systems (2022) (1)
Singular free boundary problems and local bifurcation theory (1989) (1)
Discrete Time: Filtering Algorithms (2015) (0)
Homogenization for SDEs: The Convergence Theorem (2008) (0)
Diffusive Optical Tomography in the Bayesian Framework | Multiscale Modeling & Simulation | Vol. 18, No. 2 | Society for Industrial and Applied Mathematics (2020) (0)
Weak approximation of an elliptic inverse problem (2010) (0)
Averaging for Linear Transport and Parabolic PDEs: The Convergence Theorem (2008) (0)
Optimal scaling of MCMC for conditioned diffusions (2008) (0)
Stochastic Differential Equations (2008) (0)
Data assimilation for Navier-Stokes (2011) (0)
Determining white noise forcing from Eulerian observations in the Navier-Stokes equation (2014) (0)
Solution of discrete convection–diffusion problems (2014) (0)
A First Course in Continuum Mechanics: Tensor Algebra (2008) (0)
Similarity Solutions of a Heat Equation with Nonlinearly Varying Heat Capacity (1988) (0)
Γ-Limit for Transition Paths of Maximal Probability (2012) (0)
Hierarchical Bayesian level set inversion (2016) (0)
Probing Probability Measures In High Dimensions (2012) (0)
A First Course in Continuum Mechanics: Balance Laws (2008) (0)
DIFFUSIVE OPTICAL TOMOGRAPHY IN THE BAYESIAN FRAMEWORK\ast (2020) (0)
Optimal tuning of MCMC algorithms in infinite dimensions (2008) (0)
A Multiscale Analysis of Diffusions on Rapidly Varying Surfaces (2015) (0)
Discrete Time: MATLAB Programs (2015) (0)
The convection–diffusion equation (2014) (0)
MCMC methods in high dimension. (2009) (0)
Accuracy and Stability of Filters for Data Assimilation (2018) (0)
A ug 2 00 9 Sampling Conditioned Hypoelliptic Diffusions (2009) (0)
Using mechanistic machine learning to forecast glucose and infer physiologic phenotypes in the ICU: what is possible and what are the challenges (2018) (0)
Averaging for Markov Chains (2008) (0)
A First Course in Continuum Mechanics: Bibliography (2008) (0)
Corrigendum: Volterra integral equations and a new Gronwall inequality (Part 1: The linear case) (1989) (0)
Complete Solutions Manual for A First Course in Continuum Mechanics (2017) (0)
Continuous Time: Formulation (2015) (0)
PR ] 2 3 Ju n 20 17 Gaussian Approximations for Probability Measures on R (2017) (0)
Ja n 20 06 Analysis of SPDEs Arising in Path Sampling Part II : The Nonlinear Case February 27 , 2008 (0)
Isothermal Solid Mechanics (2008) (0)
Discrete Time: Smoothing Algorithms (2015) (0)
The hybrid Monte Carlo algorithm on Hilbert space (2010) (0)
A First Course in Continuum Mechanics: Thermal Solid Mechanics (2008) (0)
The 3DVAR filter for the Navier-Stokes equation: Accuracy and stability in the limit of high-frequency observations (2012) (0)
Using data assimilation to forecast post-meal glucose for patients with type 2 diabetes (2016) (0)
Thermal Fluid Mechanics (2008) (0)
An elliptic inverse problem arising in groundater flow (2011) (0)
ACCURATE DATA ASSIMILATION FOR CHAOTIC DYNAMICAL SYSTEMS (2013) (0)
Convergence and stability in the numerical approximation ofdynamical systems (1997) (0)
Homogenization for Parabolic PDEs (2008) (0)
Estimating model error using sparsity-promoting ensemble Kalman inversion (2020) (0)
Averaging of Stochastic Differential Equations: Kurtz's Theorem Revisited (2004) (0)
INVERSE OPTIMAL TRANSPORT \ast (2020) (0)
Green’s Functions by Monte Carlo (2009) (0)
N A ] 11 S ep 2 00 9 Approximation of Bayesian Inverse Problems for PDEs September 11 (2009) (0)
The Stokes equations (2014) (0)
Invariant Manifolds for ODEs (2008) (0)
Lessons learned from assimilating knowledge into machine learning to forecast and control glucose in a critical care setting (2020) (0)
1991 Leiden Conf on Nonlinear Diffusion (1991) (0)
Bayesian inverse problems in PDEs (2009) (0)
Data Assimilation: New Challenges in Random and Stochastic Dynamical Systems (2015) (0)
Data assimilation : inverse problems in dynamical systems (2008) (0)
Hybrid Monte Carlo : geometric integration and statistics (2010) (0)
O C ] 1 M ar 2 01 3 Ensemble Kalman Methods for Inverse Problems (2013) (0)
Derivative-Free Bayesian Inversion Using Multiscale Dynamics | SIAM Journal on Applied Dynamical Systems | Vol. 21, No. 1 | Society for Industrial and Applied Mathematics (2022) (0)
The Ensemble Kalman Filter in the Near-Gaussian Setting (2022) (0)
A First Course in Continuum Mechanics: Tensor Calculus (2008) (0)
Solution of discrete Poisson problems (2014) (0)
Sti Oscillatory Systems , Delta Jumps and WhiteNoiseB (2011) (0)
Noisy gradient flow from a random walk in Hilbert space (2014) (0)
Non-stationary phase of the MALA algorithm (2018) (0)
Explorer Uncertainty quantification in graph-based classification of high dimensional data (2018) (0)
Kalman filtering and smoothing for an advection equation with model error (2011) (0)
Why predicting postprandial glucose using self-monitoring data is difficult (2017) (0)
Continuous Time: Smoothing Algorithms (2015) (0)
A P ] 8 F eb 2 01 2 Sparse Deterministic Approximation of Bayesian Inverse Problems (0)
Gradient Flows for Sampling: Mean-Field Models, Gaussian Approximations and Affine Invariance (2023) (0)
Infinite Dimensional Random Dynamical Systems and Their Applications (2008) (0)
Averaging for Markov Chains: The Convergence Theorem (2008) (0)
Averaging of Stochastic Differential Equations (2004) (0)
Averaging for Linear Transport and Parabolic PDEs (2008) (0)
Averaging for SDEs: The Convergence Theorem (2008) (0)
VARIATIONAL DATA ASSIMILATION USINGTARGETTED RANDOMWALKS 1 Variational Data Assimilation Using Targetted Random Walks (2010) (0)
Homogenization for Elliptic PDEs: The Convergence Theorem (2008) (0)
A mathematical framework for data assimilation (2009) (0)
A First Course in Continuum Mechanics: Isothermal Fluid Mechanics (2008) (0)
Homogenization for ODEs and SDEs (2008) (0)
A First Course in Continuum Mechanics: Kinematics (2008) (0)
Introduction To Gaussian Process Regression In Bayesian Inverse Problems, With New ResultsOn Experimental Design For Weighted Error Measures (2023) (0)
Continuous Time: MATLAB Programs (2015) (0)
Sequential Monte Carlo methods for Bayesian elliptic inverse problems (2015) (0)
Bayesian well-posedness for inverse problems (2010) (0)
28 M ar 2 00 6 PARAMETER ESTIMATION FOR MULTISCALE DIFFUSIONS (0)
Filtering the Navier-Stokes equation (2011) (0)
The Mean Field Ensemble Kalman Filter: Near-Gaussian Setting (2022) (0)
Invariant Manifolds for ODEs: The Convergence Theorem (2008) (0)
Partial Differential Equations (2008) (0)
Homogenization of elasticity problems on periodic composite structures (2016) (0)
Averaging for ODEs and SDEs (2008) (0)

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What Schools Are Affiliated With Andrew M. Stuart?

Andrew M. Stuart is affiliated with the following schools: