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View article: Surrogate model for Bayesian optimal experimental design for adsorption isotherm parameters in chromatography
Surrogate model for Bayesian optimal experimental design for adsorption isotherm parameters in chromatography Open
We applied Bayesian Optimal Experimental Design (B-OED) in the estimation of parameters involved in the Equilibrium Dispersive Model for chromatography with two components with the Langmuir adsorption isotherm. The parameters estimated wer…
View article: Approximation of differential entropy in Bayesian optimal experimental design
Approximation of differential entropy in Bayesian optimal experimental design Open
Bayesian optimal experimental design provides a principled framework for selecting experimental settings that maximize obtained information. In this work, we focus on estimating the expected information gain in the setting where the differ…
View article: Bayesian optimal experimental design with Wasserstein information criteria
Bayesian optimal experimental design with Wasserstein information criteria Open
Bayesian optimal experimental design (OED) provides a principled framework for selecting the most informative observational settings in experiments. With rapid advances in computational power, Bayesian OED has become increasingly feasible …
View article: Gradient-Based Non-Linear Inverse Learning
Gradient-Based Non-Linear Inverse Learning Open
We study statistical inverse learning in the context of nonlinear inverse problems under random design. Specifically, we address a class of nonlinear problems by employing gradient descent (GD) and stochastic gradient descent (SGD) with mi…
View article: Learning sparsity-promoting regularizers for linear inverse problems
Learning sparsity-promoting regularizers for linear inverse problems Open
This paper introduces a novel approach to learning sparsity-promoting regularizers for solving linear inverse problems. We develop a bilevel optimization framework to select an optimal synthesis operator, denoted as $B$, which regularizes …
View article: The GPU-based High-order adaptive OpticS Testbench
The GPU-based High-order adaptive OpticS Testbench Open
The GPU-based High-order adaptive OpticS Testbench (GHOST) at the European Southern Observatory (ESO) is a new 2-stage extreme adaptive optics (XAO) testbench at ESO. The GHOST is designed to investigate and evaluate new control methods (m…
View article: The MICADO first light imager for the ELT: overview and current status
The MICADO first light imager for the ELT: overview and current status Open
The highest scientific return, for adaptive optics (AO) observations, is\nachieved with a reliable reconstruction of the PSF. This is especially true for\nMICADO@ELT. In this presentation, we will focus on extending the MICADO PSF\nreconst…
View article: Power of prediction: spatiotemporal Gaussian process modeling for predictive control in slope-based wavefront sensing
Power of prediction: spatiotemporal Gaussian process modeling for predictive control in slope-based wavefront sensing Open
Time delay error is a significant error source in adaptive optics (AO) systems. It arises from the latency between sensing the wavefront and applying the correction. Predictive control algorithms reduce the time delay error, providing sign…
View article: Surrogate model for Bayesian optimal experimental design in chromatography
Surrogate model for Bayesian optimal experimental design in chromatography Open
We applied Bayesian Optimal Experimental Design (OED) in the estimation of parameters involved in the Equilibrium Dispersive Model for chromatography with two components with the Langmuir adsorption isotherm. The coefficients estimated wer…
View article: The power of prediction: spatiotemporal Gaussian process modeling for predictive control in slope-based wavefront sensing
The power of prediction: spatiotemporal Gaussian process modeling for predictive control in slope-based wavefront sensing Open
Time-delay error is a significant error source in adaptive optics (AO) systems. It arises from the latency between sensing the wavefront and applying the correction. Predictive control algorithms reduce the time-delay error, providing sign…
View article: An Unconditional Representation of the Conditional Score in Infinite-Dimensional Linear Inverse Problems
An Unconditional Representation of the Conditional Score in Infinite-Dimensional Linear Inverse Problems Open
Score-based diffusion models (SDMs) have emerged as a powerful tool for sampling from the posterior distribution in Bayesian inverse problems. However, existing methods often require multiple evaluations of the forward mapping to generate …
View article: Laboratory experiments of model-based reinforcement learning for adaptive optics control
Laboratory experiments of model-based reinforcement learning for adaptive optics control Open
Direct imaging of Earth-like exoplanets is one of the most prominent scientific drivers of the next generation of ground-based telescopes. Typically, Earth-like exoplanets are located at small angular separations from their host stars, mak…
View article: Laboratory Experiments of Model-based Reinforcement Learning for Adaptive Optics Control
Laboratory Experiments of Model-based Reinforcement Learning for Adaptive Optics Control Open
Direct imaging of Earth-like exoplanets is one of the most prominent scientific drivers of the next generation of ground-based telescopes. Typically, Earth-like exoplanets are located at small angular separations from their host stars, mak…
View article: Statistical inverse learning problems with random observations
Statistical inverse learning problems with random observations Open
We provide an overview of recent progress in statistical inverse problems with random experimental design, covering both linear and nonlinear inverse problems. Different regularization schemes have been studied to produce robust and stable…
View article: Bayesian design of measurements for magnetorelaxometry imaging <sup>*</sup>
Bayesian design of measurements for magnetorelaxometry imaging <sup>*</sup> Open
The aim of magnetorelaxometry imaging is to determine the distribution of magnetic nanoparticles inside a subject by measuring the relaxation of the superposition magnetic field generated by the nanoparticles after they have first been ali…
View article: Stability estimates for the expected utility in Bayesian optimal experimental design
Stability estimates for the expected utility in Bayesian optimal experimental design Open
We study stability properties of the expected utility function in Bayesian optimal experimental design. We provide a framework for this problem in a non-parametric setting and prove a convergence rate of the expected utility with respect t…
View article: Stability estimates for the expected utility in Bayesian optimal experimental design
Stability estimates for the expected utility in Bayesian optimal experimental design Open
We study stability properties of the expected utility function in Bayesian optimal experimental design. We provide a framework for this problem in a non-parametric setting and prove a convergence rate of the expected utility with respect t…
View article: Bayesian design of measurements for magnetorelaxometry imaging
Bayesian design of measurements for magnetorelaxometry imaging Open
The aim of magnetorelaxometry imaging is to determine the distribution of magnetic nanoparticles inside a subject by measuring the relaxation of the superposition magnetic field generated by the nanoparticles after they have first been ali…
View article: Bayesian Posterior Perturbation Analysis with Integral Probability Metrics
Bayesian Posterior Perturbation Analysis with Integral Probability Metrics Open
In recent years, Bayesian inference in large-scale inverse problems found in science, engineering and machine learning has gained significant attention. This paper examines the robustness of the Bayesian approach by analyzing the stability…
View article: 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 Open
Bayesian posterior distributions arising in modern applications, including inverse problems in partial differential equation models in tomography and subsurface flow, are often computationally intractable due to the large computational cos…
View article: Convex regularization in statistical inverse learning problems
Convex regularization in statistical inverse learning problems Open
We consider a statistical inverse learning problem, where the task is to estimate a function f based on noisy point evaluations of Af, where A is a linear operator. The function Af is evaluated at i.i.d. random design points u(n), n = 1, .…
View article: Least squares approximations in linear statistical inverse learning problems
Least squares approximations in linear statistical inverse learning problems Open
Statistical inverse learning aims at recovering an unknown function $f$ from randomly scattered and possibly noisy point evaluations of another function $g$, connected to $f$ via an ill-posed mathematical model. In this paper we blend stat…
View article: Stability estimates for the expected utility in Bayesian optimal experimental design
Stability estimates for the expected utility in Bayesian optimal experimental design Open
We study stability properties of the expected utility function in Bayesian optimal experimental design. We provide a framework for this problem in a non-parametric setting and prove a convergence rate of the expected utility with respect t…
View article: LBT SOUL data as a science test bench for MICADO PSF-R tool
LBT SOUL data as a science test bench for MICADO PSF-R tool Open
Current state-of-the-art adaptive optics (AO) provides ground-based, diffraction-limited observations with high Strehl ratios (SR). However, a detailed knowledge of the point spread function (PSF) is required to fully exploit the scientifi…
View article: Status of the PSF Reconstruction Work Package for MICADO ELT
Status of the PSF Reconstruction Work Package for MICADO ELT Open
MICADO is a workhorse instrument for the ESO ELT, allowing first light capability for diffraction limited imaging and long-slit spectroscopy at near-infrared wavelengths. The PSF Reconstruction (PSF-R) Team of MICADO is currently implement…
View article: Point spread function reconstruction for SOUL + LUCI LBT data
Point spread function reconstruction for SOUL + LUCI LBT data Open
Here, we present the status of an ongoing project aimed at developing a point spread function (PSF) reconstruction software for adaptive optics (AO) observations. In particular, we test for the first time the implementation of pyramid wave…
View article: Point spread function reconstruction for SOUL+LUCI LBT data
Point spread function reconstruction for SOUL+LUCI LBT data Open
This paper presents the status of an ongoing project aimed at developing a PSF reconstruction software for adaptive optics (AO) observations. In particular, we test for the first time the implementation of pyramid wave-front sensor data on…
View article: Toward on-sky adaptive optics control using reinforcement learning
Toward on-sky adaptive optics control using reinforcement learning Open
Context. The direct imaging of potentially habitable exoplanets is one prime science case for the next generation of high contrast imaging instruments on ground-based, extremely large telescopes. To reach this demanding science goal, the i…
View article: Edge-Promoting Adaptive Bayesian Experimental Design for X-ray Imaging
Edge-Promoting Adaptive Bayesian Experimental Design for X-ray Imaging Open
Funding Information: The work of the first author was supported by the the Academy of Finland through grants 320082 and 326961. The work of the second and third authors was supported by the Academy of Finland through grant 312124. School o…