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Björn Müller

Publications and source records attributed to Björn Müller.

5 recordsLinked to original sources

Holographic X-ray Phase Contrast Imaging with Partial Coherence: Uniqueness and Reconstructions from Intensity Correlations

Holographic coherent X-ray imaging enables nanoscale imaging of biological cells and tissues, rendering both phase and absorption contrast, i.e. real and imaginary parts of the refractive index. Unlike the standard model, which assumes a perfectly coherent incident beam, we consider partial coherence characterized by a known covariance operator. In addition, we assume time-resolved intensity measurements, granting access not only to expected intensities but also to their correlations. We investigate the information content of these correlations and analytically demonstrate that, under a symmetry-breaking condition on the sample and the illumination area, both phase and absorption contrast can be uniquely recovered in both the full and the linearized models. A key challenge in numerical reconstruction is the substantial increase in data dimensionality caused by computing intensity correlations during preprocessing. We propose a novel approach that leverages a low-rank assumption on the incident beam covariance operator, bypassing explicit correlation computation while still exploiting its full information. Numerical experiments demonstrate its feasibility, yielding accurate simultaneous reconstructions of phase and absorption contrast.

math.AP↗

Long-time behavior of exact and numerical solutions of stochastic evolution equations on the sphere

We investigate the long-time behavior of exact solutions and numerical approximations of linear stochastic evolution equations defined on the sphere. We focus on three classical models arising in mathematical physics: the stochastic wave equation, the stochastic Schrödinger equation, and the stochastic Maxwell's equations. For these SPDEs, we analyze several widely used time integrators with respect to trace formulas describing the evolution of physically relevant quantities such as energy, mass, and momentum dependent on the forcing term. In particular, we prove that the forward and backward Euler-Maruyama schemes fail to reproduce the correct long-time behavior of the exact solutions. In addition, we prove that the stochastic exponential integrator preserves the correct long-time behavior of the physical quantities of interest. Finally, several numerical experiments are provided to illustrate our theoretical findings.

math.NA↗

Isotropic Q-fractional Brownian motion on the sphere: regularity and fast simulation

As an extension of isotropic Gaussian random fields and Q-Wiener processes on d-dimensional spheres, isotropic Q-fractional Brownian motion is introduced and sample Hölder regularity in space-time is shown depending on the regularity of the spatial covariance operator Q and the Hurst parameter H. The processes are approximated by a spectral method in space for which strong and almost sure convergence are shown. The underlying sample paths of fractional Brownian motion are simulated by circulant embedding or conditionalized random midpoint displacement. Temporal accuracy and computational complexity are numerically tested, the latter matching the complexity of simulating a Q-Wiener process if allowing for a temporal error.

math.PR↗

Quantitative passive imaging by iterative holography: The example of helioseismic holography

In passive imaging, one attempts to reconstruct some coefficients in a wave equation from correlations of observed randomly excited solutions to this wave equation. Many methods proposed for this class of inverse problem so far are only qualitative, e.g., trying to identify the support of a perturbation. Major challenges are the increase in dimensionality when computing correlations from primary data in a preprocessing step, and often very poor pointwise signal-to-noise ratios. In this paper, we propose an approach that addresses both of these challenges: It works only on the primary data while implicitly using the full information contained in the correlation data, and it provides quantitative estimates and convergence by iteration. Our work is motivated by helioseismic holography, a well-established imaging method to map heterogenities and flows in the solar interior. We show that the back-propagation used in classical helioseismic holography can be interpreted as the adjoint of the Fréchet derivative of the operator which maps the properties of the solar interior to the correlation data on the solar surface. The theoretical and numerical framework for passive imaging problems developed in this paper extends helioseismic holography to nonlinear problems and allows for quantitative reconstructions. We present a proof of concept in uniform media.

math.NA↗

Practical Recommendations for the Design of Automatic Fault Detection Algorithms Based on Experiments with Field Monitoring Data

Automatic fault detection (AFD) is a key technology to optimize the Operation and Maintenance of photovoltaic (PV) systems portfolios. A very common approach to detect faults in PV systems is based on the comparison between measured and simulated performance. Although this approach has been explored by many authors, due to the lack a common basis for evaluating their performance, it is still unclear what are the influencing aspects in the design of AFD algorithms. In this study, a series of AFD algorithms have been tested under real operating conditions, using monitoring data collected over 58 months on 80 rooftop-type PV systems installed in Germany. The results shown that this type of AFD algorithm have the potential to detect up to 82.8% of the energy losses with specificity above 90%. In general, the higher the simulation accuracy, the higher the specificity. The use of less accurate simulations can increase sensitivity at the cost of decreasing specificity. Analyzing the measurements individually makes the algorithm less sensitive to the simulation accuracy. The use of machine learning clustering algorithm for the statistical analysis showed exceptional ability to prevent false alerts, even in cases where the modeling accuracy is not high. If a slightly higher level of false alerts can be tolerated, the analysis of daily PR using a Shewhart chart provides the high sensitivity with an exceptionally simple solution with no need for more complex algorithms for modeling or clustering.

eess.SY↗