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2019


Fast and Resource-Efficient Control of Wireless Cyber-Physical Systems
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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Ferromagnetic colloids in liquid crystal solvents

Zarubin, G.

Universität Stuttgart, Stuttgart, 2019 (phdthesis)

icm

link (url) DOI [BibTex]

link (url) DOI [BibTex]


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Fluctuating interface with a pinning potential

Pranjić, Daniel

Universität Stuttgart, Stuttgart, 2019 (mastersthesis)

icm

[BibTex]

[BibTex]


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Controlling pattern formation in the confined Schnakenberg model

Beyer, David Bernhard

Universität Stuttgart, Stuttgart, 2019 (mastersthesis)

icm

[BibTex]

[BibTex]


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Interfaces in fluids of ionic liquid crystals

Bartsch, H.

Universität Stuttgart, Stuttgart, 2019 (phdthesis)

icm

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 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.

ics

[BibTex]

[BibTex]

2018


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Pattern forming systems under confinement

Maihöfer, Michael

Universität Stuttgart, Stuttgart, 2018 (mastersthesis)

icm

[BibTex]

2018


[BibTex]


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Effective interactions between colloidal particles in critical solvents

Labbe-Laurent, M.

Universität Stuttgart, Stuttgart, 2018 (phdthesis)

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link (url) DOI [BibTex]

link (url) DOI [BibTex]


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Non-equilibrium dynamics of a binary solvent around heated colloidal particles

Wilke, Moritz

Universität Stuttgart, Stuttgart, 2018 (mastersthesis)

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[BibTex]

[BibTex]


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Monte Carlo study of colloidal structure formation at fluid interfaces

Meiler, Tim

Universität Stuttgart, Stuttgart, 2018 (mastersthesis)

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[BibTex]

[BibTex]


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Electrolyte solutions and simple fluids at curved walls

Reindl, A.

Universität Stuttgart, Stuttgart, 2018 (phdthesis)

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link (url) DOI [BibTex]

link (url) DOI [BibTex]


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Surface structure of liquid crystals

Sattler, Alexander

Universität Stuttgart, Stuttgart, 2018 (mastersthesis)

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[BibTex]

[BibTex]


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Dynamics of an active particle in confined viscous flows

Pöhnl, Ruben

Universität Stuttgart, Stuttgart, 2018 (mastersthesis)

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[BibTex]

[BibTex]


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Electrostatic interaction between colloids with constant surface potentials at fluid interfaces

Bebon, Rick

Universität Stuttgart, Stuttgart, 2018 (mastersthesis)

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[BibTex]

[BibTex]

2016


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Diffusion im Zentrifugalpotential

Totikos, Vangelis

Universität Stuttgart, Stuttgart, 2016 (mastersthesis)

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[BibTex]

2016


[BibTex]


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Electromagnetic radiation in complex environments: Many body systems and background medium

Müller, Boris

Universität Stuttgart, Stuttgart, 2016 (mastersthesis)

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[BibTex]

[BibTex]


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Theory of enantiomer separation by external fields

Gehrmann, Christian

Universität Stuttgart, Stuttgart, 2016 (mastersthesis)

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[BibTex]

[BibTex]


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General properties of ionic complex fluids

Bier, M.

Universität Stuttgart, Stuttgart, 2016 (phdthesis)

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[BibTex]

[BibTex]


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Wedge wetting by an electrolyte solution

Mu\ssotter, M.

Universität Stuttgart, Stuttgart, 2016 (mastersthesis)

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[BibTex]

[BibTex]

2009


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From colloids to biophysics: applications of classical density functional theory

Roth, R.

Universität Stuttgart, Stuttgart, 2009 (phdthesis)

icm

[BibTex]

2009


[BibTex]


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Stäbchensuspensionen in Kontakt mit geometrisch strukturierten Substraten

Günther, F.

Universität Stuttgart, Stuttgart, 2009 (mastersthesis)

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[BibTex]

[BibTex]

2003


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Coexisting Phases in Binary Platelet Mixtures

Bier, M.

Universität Stuttgart, Stuttgart, 2003 (mastersthesis)

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[BibTex]

2003


[BibTex]


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Capillary forces between structured substrates

De Souza, E. J.

Universität Stuttgart, Stuttgart, 2003 (mastersthesis)

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[BibTex]

[BibTex]


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Statistical physics of stochastic geometries

Brodatzki, U.

Universität Wuppertal, Wuppertal, 2003 (phdthesis)

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[BibTex]

[BibTex]


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Colloidal Particles in Critical Fluids

Schlesener, F.

Universität Stuttgart, Stuttgart, 2003 (phdthesis)

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[BibTex]

[BibTex]


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Diffusion in quasicrystals

Mehrer, H., Galler, R., Frank, W., Blüher, R., Strohm, A.

In Quasicrystals - Structure and Physical Properties, pages: 312-337, Wiley-VCH, Weinheim, 2003 (incollection)

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[BibTex]

[BibTex]


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Structure and Solvation Forces in Binary Hard-Sphere Mixtures

Grodon, C.

Universität Stuttgart, Stuttgart, 2003 (mastersthesis)

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[BibTex]

[BibTex]

2001


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Einflußvon Teilchenbestrahlung auf die Selbst- und Interdiffusion in amorphen Fe-Zr-Legierungen

Schuler, T.

Universität Stuttgart, Stuttgart, 2001 (phdthesis)

icm

[BibTex]

2001


[BibTex]


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Diffusion im unterkühlten flüssigen und amorphen Zustand von Zr65Cu175,Ni10Al17,5

Schaaff, P.

Universität Stuttgart, Stuttgart, 2001 (phdthesis)

icm

[BibTex]

[BibTex]

2000


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Selbst- und Fremddiffusion in amorphem Si28C36N36 und Si3N4

Matics, S.

Universität Stuttgart, Stuttgart, 2000 (phdthesis)

icm

link (url) [BibTex]

2000


link (url) [BibTex]