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Probabilistic Numerical Methods for PDE-constrained Bayesian Inverse
  Problems

Probabilistic Numerical Methods for PDE-constrained Bayesian Inverse Problems

15 January 2017
Jon Cockayne
Chris J. Oates
T. Sullivan
Mark Girolami
    AI4CE
ArXivPDFHTML

Papers citing "Probabilistic Numerical Methods for PDE-constrained Bayesian Inverse Problems"

12 / 12 papers shown
Title
Data-Efficient Kernel Methods for Learning Differential Equations and Their Solution Operators: Algorithms and Error Analysis
Data-Efficient Kernel Methods for Learning Differential Equations and Their Solution Operators: Algorithms and Error Analysis
Yasamin Jalalian
Juan Felipe Osorio Ramirez
Alexander W. Hsu
Bamdad Hosseini
H. Owhadi
48
0
0
02 Mar 2025
Physics-Informed Variational State-Space Gaussian Processes
Physics-Informed Variational State-Space Gaussian Processes
Oliver Hamelijnck
Arno Solin
Theodoros Damoulas
33
0
0
20 Sep 2024
Gaussian processes for Bayesian inverse problems associated with linear
  partial differential equations
Gaussian processes for Bayesian inverse problems associated with linear partial differential equations
Tianming Bai
A. Teckentrup
K. Zygalakis
35
8
0
17 Jul 2023
Error Analysis of Kernel/GP Methods for Nonlinear and Parametric PDEs
Error Analysis of Kernel/GP Methods for Nonlinear and Parametric PDEs
Pau Batlle
Yifan Chen
Bamdad Hosseini
H. Owhadi
Andrew M. Stuart
34
17
0
08 May 2023
Images of Gaussian and other stochastic processes under closed,
  densely-defined, unbounded linear operators
Images of Gaussian and other stochastic processes under closed, densely-defined, unbounded linear operators
T. Matsumoto
T. Sullivan
38
3
0
05 May 2023
Introduction To Gaussian Process Regression In Bayesian Inverse
  Problems, With New ResultsOn Experimental Design For Weighted Error Measures
Introduction To Gaussian Process Regression In Bayesian Inverse Problems, With New ResultsOn Experimental Design For Weighted Error Measures
T. Helin
Andrew M. Stuart
A. Teckentrup
K. Zygalakis
34
4
0
09 Feb 2023
Parameter Inference based on Gaussian Processes Informed by Nonlinear
  Partial Differential Equations
Parameter Inference based on Gaussian Processes Informed by Nonlinear Partial Differential Equations
Zhao-Xia Li
Shih-Feng Yang
Jeff Wu
11
2
0
22 Dec 2022
Stochastic Processes Under Linear Differential Constraints : Application
  to Gaussian Process Regression for the 3 Dimensional Free Space Wave Equation
Stochastic Processes Under Linear Differential Constraints : Application to Gaussian Process Regression for the 3 Dimensional Free Space Wave Equation
Iain Henderson
P. Noble
O. Roustant
21
1
0
23 Nov 2021
Probabilistic Numerical Method of Lines for Time-Dependent Partial
  Differential Equations
Probabilistic Numerical Method of Lines for Time-Dependent Partial Differential Equations
Nicholas Kramer
Jonathan Schmidt
Philipp Hennig
32
18
0
22 Oct 2021
Solving and Learning Nonlinear PDEs with Gaussian Processes
Solving and Learning Nonlinear PDEs with Gaussian Processes
Yifan Chen
Bamdad Hosseini
H. Owhadi
Andrew M. Stuart
29
153
0
24 Mar 2021
A Modern Retrospective on Probabilistic Numerics
A Modern Retrospective on Probabilistic Numerics
Chris J. Oates
T. Sullivan
AI4CE
24
64
0
14 Jan 2019
Bayesian Probabilistic Numerical Methods
Bayesian Probabilistic Numerical Methods
Jon Cockayne
Chris J. Oates
T. Sullivan
Mark Girolami
24
164
0
13 Feb 2017
1