Fast, efficient and flexible particle accelerator optimisation using densely connected and invertible neural networks

Particle accelerators are enabling tools for scientific exploration and discovery in various disciplines. Finding optimized operation points for these complex machines is a challenging task, however, due to the large number of parameters involved and the underlying non-linear dynamics. Here, we introduce two families of data-driven surrogate models, based on deep and invertible neural networks, that can replace the expensive physics computer models. These models are employed in multi-objective optimisations to find Pareto optimal operation points for two fundamentally different types of particle accelerators. Our approach reduces the time-to-solution for a multi-objective accelerator optimisation up to a factor of 640 and the computational cost up to 98%. The framework established here should pave the way for future on-line and real-time multi-objective optimisation of particle accelerators

Lasso Monte Carlo, a Novel Method for High Dimensional Uncertainty Quantification

Uncertainty quantification (UQ) is an active area of research, and an essential technique used in all fields of science and engineering. The most common methods for UQ are Monte Carlo and surrogate-modelling. The former method is dimensionality independent but has slow convergence, while the latter method has been shown to yield large computational speedups with respect to Monte Carlo. However, surrogate models suffer from the so-called curse of dimensionality, and become costly to train for high-dimensional problems, where UQ might become computationally prohibitive. In this paper we present a new technique, Lasso Monte Carlo (LMC), which combines surrogate models and the multilevel Monte Carlo technique, in order to perform UQ in high-dimensional settings, at a reduced computational cost. We provide mathematical guarantees for the unbiasedness of the method, and show that LMC can converge faster than simple Monte Carlo. The theory is numerically tested with benchmarks on toy problems, as well as on a real example of UQ from the field of nuclear engineering. In all presented examples LMC converges faster than simple Monte Carlo, and computational costs are reduced by more than a factor of 5 in some cases

Fast Uncertainty Quantification of Spent Nuclear Fuel with Neural Networks

The accurate calculation and uncertainty quantification of the characteristics of spent nuclear fuel (SNF) play a crucial role in ensuring the safety, efficiency, and sustainability of nuclear energy production, waste management, and nuclear safeguards. State of the art physics-based models, while reliable, are computationally intensive and time-consuming. This paper presents a surrogate modeling approach using neural networks (NN) to predict a number of SNF characteristics with reduced computational costs compared to physics-based models. An NN is trained using data generated from CASMO5 lattice calculations. The trained NN accurately predicts decay heat and nuclide concentrations of SNF, as a function of key input parameters, such as enrichment, burnup, cooling time between cycles, mean boron concentration and fuel temperature. The model is validated against physics-based decay heat simulations and measurements of different uranium oxide fuel assemblies from two different pressurized water reactors. In addition, the NN is used to perform sensitivity analysis and uncertainty quantification. The results are in very good alignment to CASMO5, while the computational costs (taking into account the costs of generating training samples) are reduced by a factor of 10 or more. Our findings demonstrate the feasibility of using NNs as surrogate models for fast characterization of SNF, providing a promising avenue for improving computational efficiency in assessing nuclear fuel behavior and associated risks

Parameter identification and uncertainty quantification in time-dependent models, based on dynamic update laws

ZeitabhÃ$ngige Probleme spielen eine groÃe Rolle bei der Modellierung von VorgÃ$ngen in Technik und Naturwissenschaften. Die in solchen Modellen enthaltenen Parameter sind oftmals unbekannt und mÃssen daher mit Hilfe von (unvollstÃ$ndigen) fehlerbehafteten Daten bestimmt werden. Dies fÃhrt zum inversen Problem der Parameteridentifikation, welches durch die Verwendung von Offline- oder Online- Methoden gelÃst werden kann. WÃ$hrend bei Offline- Methoden die Messdaten vor Beginn der ParameterschÃ$tzung vorliegen, wird bei Online- Methoden die Parameteridentifikation simultan zur Datensammlung durchgefÃhrt. Zwei Methoden, eine Offline- und eine Online- Methode werden in dieser Arbeit vorgestellt, analysiert und angewendet. Beide basieren auf dynamischen Aktualisierungsvorschriften. Die Online- Parameteridentifikationsmethode lÃ$sst auch unvollstÃ$ndige Beobachtungen zu, und die Annahmen ans zugrundeliegende Modell sind weniger restriktiv, als in frÃheren Publikationen, wÃ$hrend die Offline- Methode die wiederholte LÃsung von Optimierungsproblemen vermeidet. Vor allem bei Offline- Methoden ist es wichtig, die Unsicherheit in den SchÃ$tzungen zu quantifizieren, nachdem die Parameter identifiziert sind. Ein dafÃr zuverlÃ$ssiges Werkzeug sind Konfidenzintervalle basierend auf Profile Likelihoods. In dieser Arbeit wird eine integrationsbasierte Methode zur Berechnung von Profile Likelihoods vorgeschlagen,diskutiert und getestet.Time-dependent problems play an important role when modeling processes in science and technology. Parameters in these models are often unknown and therefore have to be determined from (partial) noisy measurements. This leads to the inverse problem of parameter identification, which can be solved using offline or online methods. While in offline methods the data collection process has already been completed before the actual identification process, in online methods the parameter estimation is performed simultaneously to the data collection. In this thesis two methods, one online and one offline method are proposed, analyzed and applied. Both are based on dynamic update laws for the parameter and state estimates. The online parameter identification method allows for partial observations and imposes less restrictive assumptions on the model than in previous applications, whereas the offline parameter identification method circumvents the repeated solution of optimization problems. Especially for offline methods, after parameter estimation it is crucial to quantify uncertainty of these estimates by means of, e.g., confidence regions. For this purpose, profile likelihood based confidence intervals were proven to be a reliable tool. An integration based method to compute profile likelihoods is proposed, discussed and tested in this thesis.Romana BoigerZusammenfassung in deutscher SpracheAlpen Adria Universität Klagenfurt, Dissertation, 2016OeBB(VLID)241137

Exploring Temperature-Modulated Operation Mode of Metal Oxide Gas Sensors for Robust Signal Processing

Metal oxide (MOx) gas sensor signals are mainly governed by adsorption and desorption processes of oxygen and its reaction with surrounding gas molecules. Different target gases exhibit different reaction rates leading to characteristic sensor responses for specific gas species and their concentrations. In this work, we compare temperature-modulated sensor operation (TMO) with sensor operation at a single temperature. Further, we explore if under specific TMO regimes, a simple signal processing allows for quantification of gas concentrations. We specifically investigate, if the relevant information can be captured in selected discrete wavelet coefficients. In addition, we compare the results received from this wavelet features to reaction rate evaluation features