Articles with public access mandates - Andrew M StuartLearn more
Not available anywhere: 1
Approximation of dissipative partial differential equations over long time intervals
AR Humphries, DA Jones, AM Stuart
Numerical Analysis 1993, 180-207, 2020
Mandates: US National Science Foundation, US Department of Defense
Available somewhere: 101
MCMC methods for functions: modifying old algorithms to make them faster
SL Cotter, GO Roberts, AM Stuart, D White
Mandates: UK Engineering and Physical Sciences Research Council
Neural operator: Graph kernel network for partial differential equations
A Anandkumar, K Azizzadenesheli, K Bhattacharya, N Kovachki, Z Li, ...
ICLR 2020 Workshop on Integration of Deep Neural Models and Differential …, 2020
Mandates: US Department of Defense
Neural operator: Learning maps between function spaces with applications to pdes
N Kovachki, Z Li, B Liu, K Azizzadenesheli, K Bhattacharya, A Stuart, ...
Journal of Machine Learning Research 24 (89), 1-97, 2023
Mandates: US National Science Foundation, US Department of Defense
Optimal tuning of the hybrid Monte Carlo algorithm
A Beskos, N Pillai, G Roberts, JM Sanz-Serna, A Stuart
Mandates: UK Engineering and Physical Sciences Research Council, Government of Spain
Earth system modeling 2.0: A blueprint for models that learn from observations and targeted high‐resolution simulations
T Schneider, S Lan, A Stuart, J Teixeira
Geophysical Research Letters 44 (24), 12,396-12,417, 2017
Mandates: US Department of Defense, US National Aeronautics and Space Administration
Multipole graph neural operator for parametric partial differential equations
Z Li, N Kovachki, K Azizzadenesheli, B Liu, A Stuart, K Bhattacharya, ...
Advances in Neural Information Processing Systems 33, 6755-6766, 2020
Mandates: US Department of Defense
Model reduction and neural networks for parametric PDEs
K Bhattacharya, B Hosseini, NB Kovachki, AM Stuart
The SMAI journal of computational mathematics 7, 121-157, 2021
Mandates: US National Science Foundation, US Department of Defense
Analysis of the ensemble Kalman filter for inverse problems
C Schillings, AM Stuart
SIAM Journal on Numerical Analysis 55 (3), 1264-1290, 2017
Mandates: US Department of Defense, UK Engineering and Physical Sciences Research Council
Interacting Langevin diffusions: Gradient structure and ensemble Kalman sampler
A Garbuno-Inigo, F Hoffmann, W Li, AM Stuart
SIAM Journal on Applied Dynamical Systems 19 (1), 412-441, 2020
Mandates: US National Science Foundation, US Department of Defense
Spectral gaps for a Metropolis–Hastings algorithm in infinite dimensions
M Hairer, AM Stuart, SJ Vollmer
Mandates: UK Engineering and Physical Sciences Research Council
Importance sampling: Intrinsic dimension and computational cost
S Agapiou, O Papaspiliopoulos, D Sanz-Alonso, AM Stuart
Statistical Science 32 (3), 405-431, 2017
Mandates: US Department of Defense, UK Engineering and Physical Sciences Research Council
Geometric MCMC for infinite-dimensional inverse problems
A Beskos, M Girolami, S Lan, PE Farrell, AM Stuart
Journal of Computational Physics 335, 327-351, 2017
Mandates: US Department of Defense, UK Engineering and Physical Sciences Research …
The random feature model for input-output maps between banach spaces
NH Nelsen, AM Stuart
SIAM Journal on Scientific Computing 43 (5), A3212-A3243, 2021
Mandates: US National Science Foundation, US Department of Defense
How deep are deep Gaussian processes?
MM Dunlop, MA Girolami, AM Stuart, AL Teckentrup
Journal of Machine Learning Research 19 (54), 1-46, 2018
Mandates: US National Science Foundation, US Department of Defense, UK Engineering and …
Solving and learning nonlinear PDEs with Gaussian processes
Y Chen, B Hosseini, H Owhadi, AM Stuart
Journal of Computational Physics 447, 110668, 2021
Mandates: US Department of Defense
Complexity analysis of accelerated MCMC methods for Bayesian inversion
VH Hoang, C Schwab, AM Stuart
Inverse Problems 29 (8), 085010, 2013
Mandates: Swiss National Science Foundation, UK Engineering and Physical Sciences …
Ensemble Kalman inversion: a derivative-free technique for machine learning tasks
NB Kovachki, AM Stuart
Inverse Problems 35 (9), 095005, 2019
Mandates: US National Science Foundation, US Department of Defense
Sparse deterministic approximation of Bayesian inverse problems
C Schwab, AM Stuart
Inverse Problems 28 (4), 045003, 2012
Mandates: Swiss National Science Foundation
Well-posedness and accuracy of the ensemble Kalman filter in discrete and continuous time
DTB Kelly, KJH Law, AM Stuart
Nonlinearity 27 (10), 2579, 2014
Mandates: UK Engineering and Physical Sciences Research Council
Publication and funding information is determined automatically by a computer program