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A-optimal encoding weights for nonlinear inverse problems, with
  applications to the Helmholtz inverse problem

A-optimal encoding weights for nonlinear inverse problems, with applications to the Helmholtz inverse problem

7 December 2016
B. Crestel
A. Alexanderian
G. Stadler
Omar Ghattas
ArXivPDFHTML

Papers citing "A-optimal encoding weights for nonlinear inverse problems, with applications to the Helmholtz inverse problem"

6 / 6 papers shown
Title
Bayesian design of measurements for magnetorelaxometry imaging
Bayesian design of measurements for magnetorelaxometry imaging
T. Helin
Nuutti Hyvönen
Jarno Maaninen
Juha-Pekka Puska
21
18
0
31 May 2023
Stability estimates for the expected utility in Bayesian optimal
  experimental design
Stability estimates for the expected utility in Bayesian optimal experimental design
D. Duong
T. Helin
Jose Rodrigo Rojo-Garcia
24
10
0
08 Nov 2022
Derivative-Informed Neural Operator: An Efficient Framework for
  High-Dimensional Parametric Derivative Learning
Derivative-Informed Neural Operator: An Efficient Framework for High-Dimensional Parametric Derivative Learning
Thomas O'Leary-Roseberry
Peng Chen
Umberto Villa
Omar Ghattas
AI4CE
32
39
0
21 Jun 2022
Machine learning-based conditional mean filter: a generalization of the
  ensemble Kalman filter for nonlinear data assimilation
Machine learning-based conditional mean filter: a generalization of the ensemble Kalman filter for nonlinear data assimilation
Truong-Vinh Hoang
S. Krumscheid
H. Matthies
Raúl Tempone
26
7
0
15 Jun 2021
Derivative-Informed Projected Neural Networks for High-Dimensional
  Parametric Maps Governed by PDEs
Derivative-Informed Projected Neural Networks for High-Dimensional Parametric Maps Governed by PDEs
Thomas O'Leary-Roseberry
Umberto Villa
Peng Chen
Omar Ghattas
52
70
0
30 Nov 2020
Taylor approximation for chance constrained optimization problems
  governed by partial differential equations with high-dimensional random
  parameters
Taylor approximation for chance constrained optimization problems governed by partial differential equations with high-dimensional random parameters
Peng Chen
Omar Ghattas
24
18
0
19 Nov 2020
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