World's Best Scientists 2026 revealed!
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Mathematics
USA
2026

D-Index & Metrics

Mathematics

D-Index
81
Citations
25681
World Ranking
138
National Ranking
78

Research.com Recognitions

  • 2026 - Research.com Mathematics in United States Leader Award
  • 2025 - Research.com Mathematics in United States Leader Award
  • 2020 - Fellow of the Royal Society, United Kingdom
  • 2009 - SIAM Fellow For contributions to deterministic and stochastic dynamical computations.

Overview

Andrew M. Stuart is affiliated with the California Institute of Technology in the United States. Their primary field of study is Computer Science, with considerable research focused on Artificial Intelligence, Statistical and Nonlinear Physics, Statistics and Probability, Computational Theory and Mathematics, and Statistics, Probability and Uncertainty.

The scientist's main topics of work include:

  • Gaussian Processes and Bayesian Inference
  • Model Reduction and Neural Networks
  • Probabilistic and Robust Engineering Design
  • Neural Networks and Applications
  • Advanced Mathematical Modeling in Engineering
  • Markov Chains and Monte Carlo Methods
  • Composite Material Mechanics

Andrew M. Stuart has contributed extensively to scholarly publications, particularly in venues such as:

  • arXiv (Cornell University)
  • Journal of Computational Physics
  • Inverse Problems
  • SIAM Journal on Applied Dynamical Systems
  • SIAM Journal on Scientific Computing

Some of the recent papers authored or co-authored by Andrew M. Stuart include:

  • "Fourier Neural Operator for Parametric Partial Differential Equations," 2020, arXiv (Cornell University)
  • "Interacting Langevin Diffusions: Gradient Structure and Ensemble Kalman Sampler," 2020, The Caltech Institute Archives (California Institute of Technology)
  • "Multipole Graph Neural Operator for Parametric Partial Differential Equations," 2020, arXiv (Cornell University)
  • "Neural Operator: Graph Kernel Network for Partial Differential Equations," 2020, arXiv (Cornell University)
  • "Calibrate, emulate, sample," 2020, Journal of Computational Physics

The scientist's frequent co-authors include:

  • Nikola Kovachki
  • Kaushik Bhattacharya
  • Burigede Liu
  • Daniel Sanz-Alonso
  • Tapio Schneider

Andrew M. Stuart has received awards recognizing their contributions in computational and applied mathematics, including being named a Fellow of the Royal Society, United Kingdom, in 2020 and a SIAM Fellow in 2009 for contributions to deterministic and stochastic dynamical computations.

Best Publications

  • Inverse problems: A Bayesian perspective

    Andrew M. Stuart

  • Fourier Neural Operator for Parametric Partial Differential Equations

    Zongyi Li;Nikola Borislavov Kovachki;Kamyar Azizzadenesheli;Burigede liu

  • Multiscale Methods: Averaging and Homogenization

    Grigorios A. Pavliotis;Andrew M. Stuart

  • Dynamical systems and numerical analysis

    A. M. Stuart;A. R. Humphries

  • Strong Convergence of Euler-Type Methods for Nonlinear Stochastic Differential Equations

    Desmond J. Higham;Xuerong Mao;Andrew M. Stuart

  • Ergodicity for SDEs and approximations: Locally Lipschitz vector fields and degenerate noise

    J.C. Mattingly;A.M. Stuart;D.J. Higham

  • MCMC Methods for Functions: Modifying Old Algorithms to Make Them Faster

    Simon L. Cotter;Gareth O. Roberts;A. M. Stuart;David White

  • The Bayesian Approach to Inverse Problems

    Masoumeh Dashti;Andrew M. Stuart

  • Ensemble Kalman methods for inverse problems

    Marco A Iglesias;Kody J H Law;Andrew M Stuart

  • Extracting macroscopic dynamics: model problems and algorithms

    Dror Givon;Raz Kupferman;Andrew Stuart

  • Neural Operator: Graph Kernel Network for Partial Differential Equations

    Zongyi Li;Nikola B. Kovachki;Kamyar Azizzadenesheli;Burigede Liu

  • The global dynamics of discrete semilinear parabolic equations

    C. M. Elliott;A. M. Stuart

  • Optimal tuning of the hybrid Monte Carlo algorithm

    Alexandros Beskos;Natesh S. Pillai;Gareth O. Roberts;Jesus M. Sanz-Serna

  • Earth System Modeling 2.0: A Blueprint for Models That Learn From Observations and Targeted High‐Resolution Simulations

    Tapio Schneider;Shiwei Lan;Andrew Stuart;João Teixeira

  • A First Course in Continuum Mechanics

    Oscar Gonzalez;Andrew M. Stuart

  • Model Reduction and Neural Networks for Parametric PDEs

    Kaushik Bhattacharya;Bamdad Hosseini;Nikola B. Kovachki;Andrew M. Stuart

  • Data Assimilation: A Mathematical Introduction

    Kody Law;Andrew Stuart;Konstantinos Zygalakis

  • A model mechanism for the chemotactic response of endothelial cells to tumour angiogenesis factor

    M. A. J. Chaplain;A. M. Stuart

  • MCMC methods for diffusion bridges

    Alexandros Beskos;Gareth Roberts;Andrew Stuart;Jochen Voss

  • Spectral gaps for a Metropolis–Hastings algorithm in infinite dimensions

    Martin Hairer;Andrew M. Stuart;Sebastian J. Vollmer

  • The Ensemble Kalman Filter for Inverse Problems

    Marco A. Iglesias;Kody J.H. Law;Andrew M. Stuart

  • Multipole Graph Neural Operator for Parametric Partial Differential Equations

    Zongyi Li;Nikola B. Kovachki;Kamyar Azizzadenesheli;Burigede Liu

Frequent Co-Authors

Grigorios A. Pavliotis
Grigorios A. Pavliotis Imperial College London
Gareth O. Roberts
Gareth O. Roberts University of Warwick
Martin Hairer
Martin Hairer Imperial College London
Mark Girolami
Mark Girolami University of Cambridge
Charles M. Elliott
Charles M. Elliott University of Warwick
Jonathan C. Mattingly
Jonathan C. Mattingly Duke University
Tapio Schneider
Tapio Schneider California Institute of Technology
George Hripcsak
George Hripcsak Columbia University
Andrea L. Bertozzi
Andrea L. Bertozzi University of California, Los Angeles

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