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2019


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Fast and Resource-Efficient Control of Wireless Cyber-Physical Systems

Baumann, D.

KTH Royal Institute of Technology, Stockholm, Febuary 2019 (phdthesis)

ics

PDF [BibTex]

2019


PDF [BibTex]


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Novel X-ray lenses for direct and coherent imaging

Sanli, U. T.

Universität Stuttgart, Stuttgart, 2019 (phdthesis)

mms

link (url) DOI [BibTex]

link (url) DOI [BibTex]


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Actively Learning Dynamical Systems with Gaussian Processes

Buisson-Fenet, M.

Mines ParisTech, PSL Research University, 2019 (mastersthesis)

Abstract
Predicting the behavior of complex systems is of great importance in many fields such as engineering, economics or meteorology. The evolution of such systems often follows a certain structure, which can be induced, for example from the laws of physics or of market forces. Mathematically, this structure is often captured by differential equations. The internal functional dependencies, however, are usually unknown. Hence, using machine learning approaches that recreate this structure directly from data is a promising alternative to designing physics-based models. In particular, for high dimensional systems with nonlinear effects, this can be a challenging task. Learning dynamical systems is different from the classical machine learning tasks, such as image processing, and necessitates different tools. Indeed, dynamical systems can be actuated, often by applying torques or voltages. Hence, the user has a power of decision over the system, and can drive it to certain states by going through the dynamics. Actuating this system generates data, from which a machine learning model of the dynamics can be trained. However, gathering informative data that is representative of the whole state space remains a challenging task. The question of active learning then becomes important: which control inputs should be chosen by the user so that the data generated during an experiment is informative, and enables efficient training of the dynamics model? In this context, Gaussian processes can be a useful framework for approximating system dynamics. Indeed, they perform well on small and medium sized data sets, as opposed to most other machine learning frameworks. This is particularly important considering data is often costly to generate and process, most of all when producing it involves actuating a complex physical system. Gaussian processes also yield a notion of uncertainty, which indicates how sure the model is about its predictions. In this work, we investigate in a principled way how to actively learn dynamical systems, by selecting control inputs that generate informative data. We model the system dynamics by a Gaussian process, and use information-theoretic criteria to identify control trajectories that maximize the information gain. Thus, the input space can be explored efficiently, leading to a data-efficient training of the model. We propose several methods, investigate their theoretical properties and compare them extensively in a numerical benchmark. The final method proves to be efficient at generating informative data. Thus, it yields the lowest prediction error with the same amount of samples on most benchmark systems. We propose several variants of this method, allowing the user to trade off computations with prediction accuracy, and show it is versatile enough to take additional objectives into account.

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[BibTex]

[BibTex]

2012


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Virtual Human Bodies with Clothing and Hair: From Images to Animation

Guan, P.

Brown University, Department of Computer Science, December 2012 (phdthesis)

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pdf [BibTex]

2012


pdf [BibTex]


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From Pixels to Layers: Joint Motion Estimation and Segmentation

Sun, D.

Brown University, Department of Computer Science, July 2012 (phdthesis)

ps

pdf [BibTex]

pdf [BibTex]


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Wasserstoffspeicherkapazität poröser Materialien in Kryoadsorptionstanks

Schlichtenmayer, M.

Universität Stuttgart, Stuttgart, 2012 (phdthesis)

mms

link (url) [BibTex]

link (url) [BibTex]


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Behandlung stark nichtkollinearer Magnetisierungsstrukturen mit der Spin-Cluster-Entwicklung

Dietermann, F.

Universität Stuttgart, Stuttgart, 2012 (phdthesis)

mms

link (url) [BibTex]

link (url) [BibTex]


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Spinwelleninduziertes Schalten magnetischer Vortexkerne

Kammerer, M.

Universität Stuttgart, Stuttgart, 2012 (phdthesis)

mms

link (url) [BibTex]

link (url) [BibTex]


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Die Stabilität des stromtragenden Zustands in MgB2 Schichten mit modifizierter Mikrostruktur

Treiber, S.

Universität Stuttgart, Stuttgart, 2012 (phdthesis)

mms

[BibTex]

[BibTex]


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Hartmagnetische L10-FePt basierte gro\ssflächige Nanomuster mittels Nanoimprint-Lithografie

Bublat, T.

Universität Stuttgart, Stuttgart, 2012 (phdthesis)

mms

[BibTex]

[BibTex]

2007


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On the theory of magnetization dynamics of non-collinear spin systems in the s-d model

De Angeli, L.

Universität Stuttgart, Stuttgart, 2007 (mastersthesis)

mms

[BibTex]

2007


[BibTex]


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Zur ab-initio Elektronentheorie des Magnetismus bei endlichen Temperaturen

Dietermann, F.

Universität Stuttgart, Stuttgart, 2007 (mastersthesis)

mms

[BibTex]

[BibTex]


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Röntgenzirkulardichroische Untersuchungen an ferromagnetischen verdünnten Halbleitersystemen

Tietze, T.

Universität Stuttgart, Stuttgart, 2007 (mastersthesis)

mms

[BibTex]

[BibTex]


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Low-dimensional Fe on vicinal Ir(997): Growth and magnetic properties

Kawwam, M.

Universität Stuttgart, Stuttgart, 2007 (mastersthesis)

mms

[BibTex]

[BibTex]


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Micromagnetic simulations of switching processes and the role of thermal fluctuations

Macke, S.

Universität Stuttgart, Stuttgart, 2007 (mastersthesis)

mms

[BibTex]

[BibTex]


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Physisorption von Wasserstoff in neuen Materialien mit gro\sser spezifischer Oberfläche

Schmitz, B.

Universität Bonn, Bonn, 2007 (mastersthesis)

mms

[BibTex]

[BibTex]


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Towards spin injection into silicon

Dash, S. P.

Universität Stuttgart, Stuttgart, 2007 (phdthesis)

mms

link (url) [BibTex]

link (url) [BibTex]


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Bestimmung der kritischen Schichtdicken ferromagnetischer Plättchen für Eindomänenverhalten

Soehnle, S.

Universität Stuttgart, Stuttgart, 2007 (mastersthesis)

mms

[BibTex]

[BibTex]


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Zeitaufgelöste Röntgenmikroskopie an magnetischen Mikrostrukturen

Puzic, A.

Universität Stuttgart, Stuttgart, 2007 (phdthesis)

mms

link (url) [BibTex]

link (url) [BibTex]


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Vortex dynamics studied by time-resolved X-ray microscopy

Chou, K. W.

Universität Stuttgart, Stuttgart, 2007 (phdthesis)

mms

link (url) [BibTex]

link (url) [BibTex]


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Resonante magnetische Reflektometrie an Ferromagnet/Paramagnet Heterostrukturen

Ferreras Paz, V.

Universität Stuttgart, Stuttgart, 2007 (mastersthesis)

mms

[BibTex]

[BibTex]


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Herstellung und Charakterisierung dünner Niob-Schichten auf verschiedenen Substraten

Mayer, M. W. R.

Universität Stuttgart, Stuttgart, 2007 (mastersthesis)

mms

[BibTex]

[BibTex]


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Formation of hard magnetic L10-FePt/FePd monolayers from elemental multilayers

Goo, N. H.

Universität Stuttgart, Stuttgart, 2007 (phdthesis)

mms

link (url) [BibTex]

link (url) [BibTex]


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Zur ab-initio Elektronentheorie stark nichtkollinearer Spinsysteme

Köberle, I.

Universität Stuttgart, Stuttgart, 2007 (mastersthesis)

mms

[BibTex]

[BibTex]


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Theorie der Kernspektroskopie mit zirkular polarisierter Gammastrahlung

Engelhart, W.

Universität Stuttgart, Stuttgart, 2007 (mastersthesis)

mms

[BibTex]

[BibTex]


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Untersuchung der Adsorption von Wasserstoff in porösen Materialien

Hönes, K.

Universität Stuttgart, Stuttgart, 2007 (mastersthesis)

mms

[BibTex]

[BibTex]


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Untersuchung der mechanischen Eigenschaften dünner Chromschichten

Jüllig, P.

Universität Stuttgart, Stuttgart, 2007 (mastersthesis)

mms

[BibTex]

[BibTex]