Ein Quantenmodell der Signalerkennung im Hirn

In dieser Dissertation wurde ein mathematisches Modell der Signalerkennung entwickelt. Ausgehend von den Erkenntnissen der aktuellen Hirnforschung über die Funktionsweisen und Beschaffenheit des Hirns wurde der Rahmen des besagten Modells festgelegt. Da einige nachgewiesene Funktionen, wie etwa das Bindungsproblem in seinen verschiedenen Facetten keinesfalls basierend auf klassischer Physik erklärt werden können, wurde in diesem Modell Bezug auf die Quantentheorie genommen. Diese Art der Physik kann subjektiv sehr kompliziert und kaum nachvollziehbar erscheinen. Deshalb wird davon in der Regel ausserhalb der Physik kaum Gebrauch gemacht. Da andererseits die Quantentheorie die Modellierung aller an dieser Stelle betrachteten Effekte ermöglicht, wurde in dieser Arbeit auf etablierte, und nicht anzweifelbare Methoden der Quantentheorie zurückgegriffen. Beginnend mit der Schilderung aktueller Erkenntnisse der modernen Hirnforschung wird in der vorliegenden Dissertation in den folgenden Kapiteln das Modell auf mathematisch präzise Art und Weise eingeführt. Die Arbeitsweise des Modells wird später noch durch Betrachtung von Beispielen illustriert. Generell ist das hier erklärte Modell nicht konkret auf bestimmte Lebensformen, wie zum Beispiel den Menschen, zugeschnitten. Es handelt sich hierbei eher um ein universelles Modell der Signalerkennung, was nach Belieben, dafür aber mit immensem Aufwand konkretisiert werden kann. Die aktuelle Fassung bietet sozusagen das Fundament für weitere Spezialisierungen, um auch konkrete Prozesse des Hirns modellieren bzw. simulieren zu können.

HMCLab: a framework for solving diverse geophysical inverse problems using the Hamiltonian Monte Carlo method

The use of the probabilistic approach to solve inverse problems is becoming more popular in the geophysical community, thanks to its ability to address nonlinear forward problems and to provide uncertainty quantification. However, such strategy is often tailored to specific applications and therefore there is a lack of a common platform for solving a range of different geophysical inverse problems and showing potential and pitfalls. We demonstrate a common framework to solve such inverse problems ranging from, e.g, earthquake source location to potential field data inversion and seismic tomography. Within this approach, we can provide probabilities related to certain properties or structures of the subsurface. Thanks to its ability to address high-dimensional problems, the Hamiltonian Monte Carlo (HMC) algorithm has emerged as the state-of-the-art tool for solving geophysical inverse problems within the probabilistic framework. HMC requires the computation of gradients, which can be obtained by adjoint methods, making the solution of tomographic problems ultimately feasible. These results can be obtained with "HMCLab", a tool for solving a range of different geophysical inverse problems using sampling methods, focusing in particular on the HMC algorithm. HMCLab consists of a set of samplers and a set of geophysical forward problems. For each problem its misfit function and gradient computation are provided and, in addition, a set of prior models can be combined to inject additional information into the inverse problem. This allows users to experiment with probabilistic inverse problems and also address real-world studies. We show how to solve a selected set of problems within this framework using variants of the HMC algorithm and analyze the results. HMCLab is provided as an open source package written both in Python and Julia, welcoming contributions from the community.Comment: 21 pages, 4 figure

Using automated algorithm configuration to improve the optimization of decentralized energy systems modeled as large-scale, two-stage stochastic programs

The optimization of decentralized energy systems is an important practical problem that can be modeled using stochastic programs and solved via their large-scale, deterministic equivalent formulations. Unfortunately, using this approach, even when leveraging a high degree of parallelism on large high-performance computing (HPC) systems, finding close-to-optimal solutions still requires long computation. In this work, we present a procedure to reduce this computational effort substantially, using a stateof-the-art automated algorithm configuration method. We apply this procedure to a well-known example of a residential quarter with photovoltaic systems and storages, modeled as a two-stage stochastic mixed-integer linear program (MILP). We demonstrate substantially reduced computing time and costs of up to 50% achieved by our procedure. Our methodology can be applied to other, similarly-modeled energy systems

Subglacial volcano monitoring with fibre-optic sensing: Grímsvötn, Iceland

We present a distributed acoustic sensing (DAS) experiment at Grímsvötn, Iceland. This is intended to investigate volcano-microseismicity at Grímsvötn specifically, and to assess the suitability of DAS as a subglacial volcano monitoring tool in general. In spring 2021, we trenched a 12 km long fiber-optic cable into the ice sheet around and within the caldera, followed by nearly one month of continuous recording. An image processing algorithm that exploits spatial coherence in DAS data detects on average ~100 events per day, almost 2 orders of magnitude more than in the regional earthquake catalog. A nonlinear Bayesian inversion reveals the presence of pronounced seismicity clusters, containing events with magnitudes between −3.4 and 1.7. Their close proximity to surface volcanic features suggests a geothermal origin. In addition to painting a fine-scale picture of seismic activity at Grímsvötn, this work confirms the potential of DAS in subglacial volcano monitoring

Thin-Film-Based SAW Magnetic Field Sensors

In this work, the first surface acoustic-wave-based magnetic field sensor using thin-film AlScN as piezoelectric material deposited on a silicon substrate is presented. The fabrication is based on standard semiconductor technology. The acoustically active area consists of an AlScN layer that can be excited with interdigital transducers, a smoothing SiO2 layer, and a magnetostrictive FeCoSiB film. The detection limit of this sensor is 2.4 nT/Hz at 10 Hz and 72 pT/Hz at 10 kHz at an input power of 20 dBm. The dynamic range was found to span from about ±1.7 mT to the corresponding limit of detection, leading to an interval of about 8 orders of magnitude. Fabrication, achieved sensitivity, and noise floor of the sensors are presented

Bringing Stellar Evolution & Feedback Together: Summary of proposals from the Lorentz Center Workshop, 2022

Stars strongly impact their environment, and shape structures on all scales throughout the universe, in a process known as ``feedback''. Due to the complexity of both stellar evolution and the physics of larger astrophysical structures, there remain many unanswered questions about how feedback operates, and what we can learn about stars by studying their imprint on the wider universe. In this white paper, we summarize discussions from the Lorentz Center meeting `Bringing Stellar Evolution and Feedback Together' in April 2022, and identify key areas where further dialogue can bring about radical changes in how we view the relationship between stars and the universe they live in.Comment: Accepted to the Publications of the Astronomical Society of the Pacifi

Seamless GPU Acceleration for C++-Based Physics with the Metal Shading Language on Apple’s M Series Unified Chips

The M series of chips produced by Apple has proven a capable and power-efficient alternative to mainstream Intel and AMD ×86 processors for everyday tasks. In addition, the unified design integrating the central processing and graphics processing unit (GPU), have allowed these M series chips to excel at many tasks with heavy graphical requirements without the need for a discrete GPU) in some cases even outperforming discrete GPUs. In this work, we show how the M series chips can be leveraged using the Metal Shading Language (MSL) to accelerate typical array operations in C++. More important, we show how the usage of MSL avoids the typical complexity of compute unified device architecture (CUDA) or OpenACC memory management by allowing the central processing unit (CPU) and GPU to work in unified memory. We demonstrate how performant the M series chips are on standard 1D and 2D array operations such as array addition, single-precision A·X plus Y, and finite-difference stencils, with respect to serial and OpenMP-accelerated CPU code. The reduced complexity of implementing MSL also allows us to accelerate an existing elastic wave equation solver (originally based on OpenMP-accelerated C++) while retaining all CPU and OpenMP functionality without modification. The resulting performance gain of simulating the wave equation is near an order of magnitude for large domain sizes. This gain attained from using MSL is similar to other GPU-accelerated wave-propagation codes with respect to their CPU variants but does not come at much increased programming complexity that prohibits the typical scientific programmer to leverage these accelerators. This result shows how unified processing units can be a valuable tool to seismologists and computational scientists in general, lowering the bar to writing performant codes that leverage modern GPUs.ISSN:0895-0695ISSN:1938-205