Optimal experimental design for infinite-dimensional Bayesian inverse problems governed by PDEs: A review
A Alexanderian - Inverse Problems, 2021 - iopscience.iop.org
We present a review of methods for optimal experimental design (OED) for Bayesian inverse
problems governed by partial differential equations with infinite-dimensional parameters …
problems governed by partial differential equations with infinite-dimensional parameters …
A generalized conditional gradient method for dynamic inverse problems with optimal transport regularization
We develop a dynamic generalized conditional gradient method (DGCG) for dynamic
inverse problems with optimal transport regularization. We consider the framework …
inverse problems with optimal transport regularization. We consider the framework …
Tractable optimal experimental design using transport maps
K Koval, R Herzog, R Scheichl - arXiv preprint arXiv:2401.07971, 2024 - arxiv.org
We present a flexible method for computing Bayesian optimal experimental designs
(BOEDs) for inverse problems with intractable posteriors. The approach is applicable to a …
(BOEDs) for inverse problems with intractable posteriors. The approach is applicable to a …
Optimal experimental design under irreducible uncertainty for linear inverse problems governed by PDEs
K Koval, A Alexanderian, G Stadler - Inverse Problems, 2020 - iopscience.iop.org
We present a method for computing A-optimal sensor placements for infinite-dimensional
Bayesian linear inverse problems governed by PDEs with irreducible model uncertainties …
Bayesian linear inverse problems governed by PDEs with irreducible model uncertainties …
Towards optimal sensor placement for inverse problems in spaces of measures
The objective of this work is to quantify the reconstruction error in sparse inverse problems
with measures and stochastic noise, motivated by optimal sensor placement. To be useful in …
with measures and stochastic noise, motivated by optimal sensor placement. To be useful in …
Optimal design for compliance modeling of industrial robots with bayesian inference of stiffnesses
C Tepper, A Matei, J Zarges, S Ulbrich… - Production Engineering, 2023 - Springer
In this paper a cost and time efficient approach to setup a compliance model for industrial
robots is presented. The compliance model is distinctly determined by the gear's stiffness …
robots is presented. The compliance model is distinctly determined by the gear's stiffness …
Adaptive Gaussian process regression for efficient building of surrogate models in inverse problems
P Semler, M Weiser - Inverse Problems, 2023 - iopscience.iop.org
In a task where many similar inverse problems must be solved, evaluating costly simulations
is impractical. Therefore, replacing the model y with a surrogate model ys that can be …
is impractical. Therefore, replacing the model y with a surrogate model ys that can be …
On fast convergence rates for generalized conditional gradient methods with backtracking stepsize
A generalized conditional gradient method for minimizing the sum of two convex functions,
one of them differentiable, is presented. This iterative method relies on two main ingredients …
one of them differentiable, is presented. This iterative method relies on two main ingredients …
Linear convergence of accelerated conditional gradient algorithms in spaces of measures
A class of generalized conditional gradient algorithms for the solution of optimization
problem in spaces of Radon measures is presented. The method iteratively inserts …
problem in spaces of Radon measures is presented. The method iteratively inserts …
Adaptive gradient enhanced gaussian process surrogates for inverse problems
P Semler, M Weiser - arXiv preprint arXiv:2404.01864, 2024 - arxiv.org
Generating simulated training data needed for constructing sufficiently accurate surrogate
models to be used for efficient optimization or parameter identification can incur a huge …
models to be used for efficient optimization or parameter identification can incur a huge …