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ORCIDAndrew M. Stuart
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- affiliation: California Institute of Technology, Pasadena, CA, USA
- affiliation (former): University of Warwick, Department of Mathematics, Coventry, UK
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Journal Articles
- 2024
[j65]Jinlong Wu
, Matthew E. Levine, Tapio Schneider, Andrew M. Stuart:
Learning about structural errors in models of complex dynamical systems. J. Comput. Phys. 513: 113157 (2024)- [j64]Kaushik Bhattacharya, Nikola B. Kovachki, Aakila Rajan, Andrew M. Stuart, Margaret Trautner:
Learning Homogenization for Elliptic Operators. SIAM J. Numer. Anal. 62(4): 1844-1873 (2024) - [j63]Nicholas H. Nelsen, Andrew M. Stuart:
Operator Learning Using Random Features: A Tool for Scientific Computing. SIAM Rev. 66(3): 535-571 (2024) - 2023
- [j62]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): 33-84 (2023) - [j61]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: 89:1-89:97 (2023) - [j60]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): 480-513 (2023) - [j59]Kaushik Bhattacharya, Burigede Liu, Andrew M. Stuart, Margaret Trautner:
Learning Markovian Homogenized Models in Viscoelasticity. Multiscale Model. Simul. 21(2): 641-679 (2023) - 2022
- [j58]Daniel Zhengyu Huang, Tapio Schneider, Andrew M. Stuart:
Iterated Kalman methodology for inverse problems. J. Comput. Phys. 463: 111262 (2022) - [j57]Tapio Schneider, Andrew M. Stuart, Jinlong Wu:
Ensemble Kalman inversion for sparse learning of dynamical systems from time-averaged data. J. Comput. Phys. 470: 111559 (2022) - [j56]Grigorios A. Pavliotis, Andrew M. Stuart, Urbain Vaes:
Derivative-Free Bayesian Inversion Using Multiscale Dynamics. SIAM J. Appl. Dyn. Syst. 21(1): 284-326 (2022) - [j55]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): 1539-1572 (2022) - 2021
- [j54]Emmet Cleary, Alfredo Garbuno-Inigo, Shiwei Lan, Tapio Schneider, Andrew M. Stuart:
Calibrate, emulate, sample. J. Comput. Phys. 424: 109716 (2021) - [j53]Yifan Chen, Bamdad Hosseini, Houman Owhadi, Andrew M. Stuart:
Solving and learning nonlinear PDEs with Gaussian processes. J. Comput. Phys. 447: 110668 (2021) - [j52]Nikola B. Kovachki, Andrew M. Stuart:
Continuous Time Analysis of Momentum Methods. J. Mach. Learn. Res. 22: 17:1-17:40 (2021) - [j51]Yifan Chen, Houman Owhadi, Andrew M. Stuart:
Consistency of empirical Bayes and kernel flow for hierarchical parameter estimation. Math. Comput. 90(332): 2527-2578 (2021) - [j50]Nicholas H. Nelsen, Andrew M. Stuart:
The Random Feature Model for Input-Output Maps between Banach Spaces. SIAM J. Sci. Comput. 43(5): A3212-A3243 (2021) - 2020
- [j49]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: 186:1-186:55 (2020) - [j48]Kit Newton, Qin Li, Andrew M. Stuart:
Diffusive Optical Tomography in the Bayesian Framework. Multiscale Model. Simul. 18(2): 589-611 (2020) - [j47]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): 412-441 (2020) - [j46]Andrew M. Stuart, Marie-Therese Wolfram:
Inverse Optimal Transport. SIAM J. Appl. Math. 80(1): 599-619 (2020) - [j45]Neil K. Chada, Andrew M. Stuart, Xin T. Tong:
Tikhonov Regularization within Ensemble Kalman Inversion. SIAM J. Numer. Anal. 58(2): 1263-1294 (2020) - [j44]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): A1984-A2013 (2020) - 2019
- [j43]Han Cheng Lie, Andrew M. Stuart, Timothy John Sullivan:
Strong convergence rates of probabilistic integrators for ordinary differential equations. Stat. Comput. 29(6): 1265-1283 (2019) - [j42]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): 1475-1500 (2019) - 2018
- [j41]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): 1392-1401 (2018) - [j40]Matthew M. Dunlop, Mark A. Girolami, Andrew M. Stuart, Aretha L. Teckentrup:
How Deep Are Deep Gaussian Processes? J. Mach. Learn. Res. 19: 54:1-54:46 (2018) - [j39]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): 568-595 (2018) - [j38]Andrew M. Stuart, Aretha L. Teckentrup:
Posterior consistency for Gaussian process approximations of Bayesian posterior distributions. Math. Comput. 87(310): 721-753 (2018) - 2017
- [j37]Alexandros Beskos, Mark A. Girolami, Shiwei Lan, Patrick E. Farrell, Andrew M. Stuart:
Geometric MCMC for infinite-dimensional inverse problems. J. Comput. Phys. 335: 327-351 (2017) - [j36]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): 493-518 (2017) - [j35]Yulong Lu, Andrew M. Stuart, Hendrik Weber:
Gaussian Approximations for Probability Measures on Rd. SIAM/ASA J. Uncertain. Quantification 5(1): 1136-1165 (2017) - [j34]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): 1065-1082 (2017) - [j33]Matthew M. Dunlop, Marco A. Iglesias, Andrew M. Stuart:
Hierarchical Bayesian level set inversion. Stat. Comput. 27(6): 1555-1584 (2017) - [j32]Yulong Lu, Andrew M. Stuart, Hendrik Weber:
Gaussian Approximations for Transition Paths in Brownian Dynamics. SIAM J. Math. Anal. 49(4): 3005-3047 (2017) - [j31]Claudia Schillings, Andrew M. Stuart:
Analysis of the Ensemble Kalman Filter for Inverse Problems. SIAM J. Numer. Anal. 55(3): 1264-1290 (2017) - 2015
- [j30]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): 389-449 (2015) - [j29]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): 1200-1220 (2015) - [j28]Alexandros Beskos, Ajay Jasra, Ege A. Muzaffer, Andrew M. Stuart:
Sequential Monte Carlo methods for Bayesian elliptic inverse problems. Stat. Comput. 25(4): 727-737 (2015) - [j27]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): 4091-4122 (2015) - [j26]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) - 2014
- [j25]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): 511-544 (2014) - 2011
- [j24]M. Dashti, Andrew M. Stuart:
Uncertainty Quantification and Weak Approximation of an Elliptic Inverse Problem. SIAM J. Numer. Anal. 49(6): 2524-2542 (2011) - 2010
- [j23]Andrew M. Stuart:
Inverse problems: A Bayesian perspective. Acta Numer. 19: 451-559 (2010) - [j22]S. L. Cotter, M. Dashti, Andrew M. Stuart:
Approximation of Bayesian Inverse Problems for PDEs. SIAM J. Numer. Anal. 48(1): 322-345 (2010) - [j21]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): 552-577 (2010) - 2009
- [j20]Grigorios A. Pavliotis, Andrew M. Stuart, Konstantinos C. Zygalakis:
Calculating effective diffusivities in the limit of vanishing molecular diffusion. J. Comput. Phys. 228(4): 1030-1055 (2009) - [j19]Yvo Pokern, Andrew M. Stuart, Eric Vanden-Eijnden:
Remarks on Drift Estimation for Diffusion Processes. Multiscale Model. Simul. 8(1): 69-95 (2009) - 2006
- [j18]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): 403-434 (2006) - 2005
- [j17]Grigorios A. Pavliotis, Andrew M. Stuart:
Analysis of White Noise Limits for Stochastic Systems with Two Fast Relaxation Times. Multiscale Model. Simul. 4(1): 1-35 (2005) - 2003
- [j16]Desmond J. Higham, Xuerong Mao, Andrew M. Stuart:
Exponential Mean-Square Stability of Numerical Solutions to Stochastic Differential Equations. LMS J. Comput. Math. 6: 297-313 (2003) - [j15]Grigorios A. Pavliotis, Andrew M. Stuart:
White Noise Limits for Inertial Particles in a Random Field. Multiscale Model. Simul. 1(4): 527-553 (2003) - 2002
- [j14]Donald J. Estep, Andrew M. Stuart:
The dynamical behavior of the discontinuous Galerkin method and related difference schemes. Math. Comput. 71(239): 1075-1103 (2002) - [j13]Desmond J. Higham, Xuerong Mao, Andrew M. Stuart:
Strong Convergence of Euler-Type Methods for Nonlinear Stochastic Differential Equations. SIAM J. Numer. Anal. 40(3): 1041-1063 (2002) - 2001
- [j12]Begoña Cano, Andrew M. Stuart, Endre Süli, J. O. Warren:
Stiff Oscillatory Systems, Delta Jumps and White Noise. Found. Comput. Math. 1(1): 69-100 (2001) - 2000
- [j11]Tony Shardlow, Andrew M. Stuart:
A Perturbation Theory for Ergodic Markov Chains and Application to Numerical Approximations. SIAM J. Numer. Anal. 37(4): 1120-1137 (2000) - 1998
- [j10]Martin J. Gander, Andrew M. Stuart:
Space-Time Continuous Analysis of Waveform Relaxation for the Heat Equation. SIAM J. Sci. Comput. 19(6): 2014-2031 (1998) - [j9]Chris J. Budd, George P. Koomullil, Andrew M. Stuart:
On the Solution of Convection-Diffusion Boundary Value Problems Using Equidistributed Grids. SIAM J. Sci. Comput. 20(2): 591-618 (1998) - 1997
- [j8]Morten Bjørhus, Andrew M. Stuart:
Waveform relaxation as a dynamical system. Math. Comput. 66(219): 1101-1117 (1997) - [j7]Andrew M. Stuart:
Probabilistic and deterministic convergence proofs for software for initial value problems. Numer. Algorithms 14(1-3): 227-260 (1997) - 1994
- [j6]Chris J. Budd, J. William Dold, Andrew M. Stuart:
Blow-up in a System of Partial Differential Equations with Conserved First Integral. Part II: Problems with Convection. SIAM J. Appl. Math. 54(3): 610-640 (1994) - [j5]Andrew M. Stuart, A. R. Humphries:
Model Problems in Numerical Stability Theory for Initial Value Problems. SIAM Rev. 36(2): 226-257 (1994) - 1993
- [j4]Chris J. Budd, J. William Dold, Andrew M. Stuart:
Blowup in a Partial Differential Equation with Conserved First Integral. SIAM J. Appl. Math. 53(3): 718-742 (1993) - [j3]Fengshan Bai, Alastair Spence, Andrew M. Stuart:
The Numerical Computation of Heteroclinic Connections in Systems of Gradient Partial Differential Equations. SIAM J. Appl. Math. 53(3): 743-769 (1993) - 1991
- [j2]Andrew M. Stuart, A. T. Peplow:
The Dynamics of the Theta Method. SIAM J. Sci. Comput. 12(6): 1351-1372 (1991) - 1989
- [j1]Andrew M. Stuart:
Nonlinear Instability in Dissipative Finite Difference Schemes. SIAM Rev. 31(2): 191-220 (1989)
Conference and Workshop Papers
- 2022
- [c8]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 - 2021
- [c7]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 - 2020
- [c6]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 - [c5]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 - 2019
- [c4]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 - 2018
- [c3]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 - 2017
- [c2]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 - 2016
- [c1]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
Data and Artifacts
- 2022
- [d1]Zongyi Li, Miguel Liu-Schiaffini, Nikola Borislavov Kovachki, Burigede Liu, Kamyar Azizzadenesheli, Kaushik Bhattacharya, Andrew M. Stuart, Anima Anandkumar:
Learning Dissipative Dynamics in Chaotic Systems (Datasets). Zenodo, 2022
Informal and Other Publications
- 2024
- [i42]Jinlong Wu, Matthew E. Levine, Tapio Schneider, Andrew M. Stuart:
Learning About Structural Errors in Models of Complex Dynamical Systems. CoRR abs/2401.00035 (2024) - [i41]Nikola B. Kovachki, Samuel Lanthaler, Andrew M. Stuart:
Operator Learning: Algorithms and Analysis. CoRR abs/2402.15715 (2024) - [i40]Samuel Lanthaler, Andrew M. Stuart, Margaret Trautner:
Discretization Error of Fourier Neural Operators. CoRR abs/2405.02221 (2024) - [i39]Yifan Chen, Bamdad Hosseini, Houman Owhadi, Andrew M. Stuart:
Gaussian Measures Conditioned on Nonlinear Observations: Consistency, MAP Estimators, and Simulation. CoRR abs/2405.13149 (2024) - [i38]Ömer Deniz Akyildiz, Mark Girolami, Andrew M. Stuart, Arnaud Vadeboncoeur:
Efficient Prior Calibration From Indirect Data. CoRR abs/2405.17955 (2024) - [i37]Edoardo Calvello, Nikola B. Kovachki, Matthew E. Levine, Andrew M. Stuart:
Continuum Attention for Neural Operators. CoRR abs/2406.06486 (2024) - [i36]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 abs/2406.17263 (2024) - 2023
- [i35]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 abs/2302.04518 (2023) - [i34]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 abs/2302.11024 (2023) - [i33]Samuel Lanthaler, Zongyi Li, Andrew M. Stuart:
The Nonlocal Neural Operator: Universal Approximation. CoRR abs/2304.13221 (2023) - [i32]Pau Batlle, Yifan Chen, Bamdad Hosseini, Houman Owhadi, Andrew M. Stuart:
Error Analysis of Kernel/GP Methods for Nonlinear and Parametric PDEs. CoRR abs/2305.04962 (2023) - [i31]Kaushik Bhattacharya, Nikola B. Kovachki, Aakila Rajan, Andrew M. Stuart, Margaret Trautner:
Learning Homogenization for Elliptic Operators. CoRR abs/2306.12006 (2023) - [i30]Samuel Lanthaler, Andrew M. Stuart:
The curse of dimensionality in operator learning. CoRR abs/2306.15924 (2023) - [i29]Yifan Chen, Daniel Zhengyu Huang, Jiaoyang Huang, Sebastian Reich, Andrew M. Stuart:
Sampling via Gradient Flows in the Space of Probability Measures. CoRR abs/2310.03597 (2023) - [i28]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 abs/2310.14555 (2023) - 2022
- [i27]Maarten V. de Hoop, Daniel Zhengyu Huang, Elizabeth Qian, Andrew M. Stuart:
The Cost-Accuracy Trade-Off In Operator Learning With Neural Networks. CoRR abs/2203.13181 (2022) - [i26]Daniel Zhengyu Huang, Jiaoyang Huang, Sebastian Reich, Andrew M. Stuart:
Efficient Derivative-free Bayesian Inference for Large-Scale Inverse Problems. CoRR abs/2204.04386 (2022) - [i25]Kaushik Bhattacharya, Burigede Liu, Andrew M. Stuart, Margaret Trautner:
Learning Markovian Homogenized Models in Viscoelasticity. CoRR abs/2205.14139 (2022) - [i24]Ziming Liu, Andrew M. Stuart, Yixuan Wang:
Second Order Ensemble Langevin Method for Sampling and Inverse Problems. CoRR abs/2208.04506 (2022) - [i23]Edoardo Calvello, Sebastian Reich, Andrew M. Stuart:
Ensemble Kalman Methods: A Mean Field Perspective. CoRR abs/2209.11371 (2022) - 2021
- [i22]Daniel Zhengyu Huang, Tapio Schneider, Andrew M. Stuart:
Unscented Kalman Inversion. CoRR abs/2102.01580 (2021) - [i21]Yifan Chen, Bamdad Hosseini, Houman Owhadi, Andrew M. Stuart:
Solving and Learning Nonlinear PDEs with Gaussian Processes. CoRR abs/2103.12959 (2021) - [i20]Andrew B. Duncan, Andrew M. Stuart, Marie-Therese Wolfram:
Ensemble Inference Methods for Models With Noisy and Expensive Likelihoods. CoRR abs/2104.03384 (2021) - [i19]José Antonio Carrillo, Franca Hoffmann, Andrew M. Stuart, Urbain Vaes:
Consensus Based Sampling. CoRR abs/2106.02519 (2021) - [i18]Zongyi Li, Nikola B. Kovachki, Kamyar Azizzadenesheli, Burigede Liu, Kaushik Bhattacharya, Andrew M. Stuart, Anima Anandkumar:
Markov Neural Operators for Learning Chaotic Systems. CoRR abs/2106.06898 (2021) - [i17]Matthew E. Levine, Andrew M. Stuart:
A Framework for Machine Learning of Model Error in Dynamical Systems. CoRR abs/2107.06658 (2021) - [i16]Nikola B. Kovachki, Zongyi Li, Burigede Liu, Kamyar Azizzadenesheli, Kaushik Bhattacharya, Andrew M. Stuart, Anima Anandkumar:
Neural Operator: Learning Maps Between Function Spaces. CoRR abs/2108.08481 (2021) - [i15]Maarten V. de Hoop, Nikola B. Kovachki, Nicholas H. Nelsen, Andrew M. Stuart:
Convergence Rates for Learning Linear Operators from Noisy Data. CoRR abs/2108.12515 (2021) - 2020
- [i14]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 abs/2003.03485 (2020) - [i13]Kaushik Bhattacharya, Bamdad Hosseini, Nikola B. Kovachki, Andrew M. Stuart:
Model Reduction and Neural Networks for Parametric PDEs. CoRR abs/2005.03180 (2020) - [i12]Nicholas H. Nelsen, Andrew M. Stuart:
The Random Feature Model for Input-Output Maps between Banach Spaces. CoRR abs/2005.10224 (2020) - [i11]Yifan Chen, Houman Owhadi, Andrew M. Stuart:
Consistency of Empirical Bayes And Kernel Flow For Hierarchical Parameter Estimation. CoRR abs/2005.11375 (2020) - [i10]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 abs/2006.09535 (2020) - [i9]Andrea L. Bertozzi, Bamdad Hosseini, Hao Li, Kevin Miller, Andrew M. Stuart:
Posterior Consistency of Semi-Supervised Regression on Graphs. CoRR abs/2007.12809 (2020) - [i8]Assyr Abdulle, Giacomo Garegnani, Grigorios A. Pavliotis, Andrew M. Stuart, Andrea Zanoni:
Drift Estimation of Multiscale Diffusions Based on Filtered Data. CoRR abs/2009.13457 (2020) - [i7]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 abs/2010.08895 (2020) - 2019
- [i6]Nikola B. Kovachki, Andrew M. Stuart:
Analysis Of Momentum Methods. CoRR abs/1906.04285 (2019) - [i5]Franca Hoffmann, Bamdad Hosseini, Zhi Ren, Andrew M. Stuart:
Consistency of semi-supervised learning algorithms on graphs: Probit and one-hot methods. CoRR abs/1906.07658 (2019) - 2018
- [i4]Matthew M. Dunlop, Dejan Slepcev, Andrew M. Stuart, Matthew Thorpe:
Large Data and Zero Noise Limits of Graph-Based Semi-Supervised Learning Algorithms. CoRR abs/1805.09450 (2018) - [i3]Nikola B. Kovachki, Andrew M. Stuart:
Ensemble Kalman Inversion: A Derivative-Free Technique For Machine Learning Tasks. CoRR abs/1808.03620 (2018) - 2017
- [i2]Andrea L. Bertozzi, Xiyang Luo, Andrew M. Stuart, Konstantinos C. Zygalakis:
Uncertainty Quantification in the Classification of High Dimensional Data. CoRR abs/1703.08816 (2017) - 2011
- [i1]Kody J. H. Law, Andrew M. Stuart:
Evaluating Data Assimilation Algorithms. CoRR abs/1107.4118 (2011)
Coauthor Index
Nikola B. Kovachki
aka: Nikola Borislavov Kovachki
aka: Nikola Borislavov Kovachki
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