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5 results (View BibTeX file of all listed publications)

2015

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Learning robots

Trimpe, S.

2015 (mpi_year_book)

Abstract
An exploded power plant, collapsed buildings after an earthquake, a burning vehicle loaded with hazardous goods – all of these are dangerous situations for human emergency responders. What if we could send robots instead of humans? Researchers at the Autonomous Motion Department work on fundamental principles required to build intelligent robots which one day can help us in dangerous situations. A key requirement for making this happen is that robots must be enabled to learn.

link (url) [BibTex]

2015

Trimpe, S. Learning robots 2015 (mpi_year_book)

link (url) [BibTex]

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The smallest human-made nano-motor

Sánchez, Samuel

2015 (mpi_year_book)

Abstract
Tiny self-propelled motors which speed through the water and clean up pollutions along the way or small robots which can swim effortlessly through blood to one day transport medication to a certain part of the body – this sounds like taken from a science fiction movie script. However, Samuel Sánchez is already hard at work in his lab at the Max Planck Institute for Intelligent Systems in Stuttgart to make these visions come true. Self-propelled micro-nanorobots and the usage as integrated sensors in microfluid-chips: that’s the topic of Sánchez` research group.

link (url) [BibTex]

Sánchez, Samuel The smallest human-made nano-motor 2015 (mpi_year_book)

link (url) [BibTex]

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Derivation of phenomenological expressions for transition matrix elements for electron-phonon scattering

Illg, C., Haag, M., Müller, B. Y., Czycholl, G., Fähnle, M.

2015 (misc)

mms

link (url) [BibTex]

2004

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Statistische Lerntheorie und Empirische Inferenz

Schölkopf, B.

Jahrbuch der Max-Planck-Gesellschaft, 2004, pages: 377-382, 2004 (misc)

Abstract
Statistical learning theory studies the process of inferring regularities from empirical data. The fundamental problem is what is called generalization: how it is possible to infer a law which will be valid for an infinite number of future observations, given only a finite amount of data? This problem hinges upon fundamental issues of statistics and science in general, such as the problems of complexity of explanations, a priori knowledge, and representation of data.

ei

PDF Web [BibTex]

2004

ei Schölkopf, B. Statistische Lerntheorie und Empirische Inferenz Jahrbuch der Max-Planck-Gesellschaft, 2004, pages: 377-382, 2004 (misc)

PDF Web [BibTex]

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Nanoscale Materials for Energy Storage
{Materials Science \& Engineering B}, 108, pages: 292, Elsevier, 2004 (misc)

mms

[BibTex]

mms Nanoscale Materials for Energy Storage {Materials Science \& Engineering B}, 108, pages: 292, Elsevier, 2004 (misc)

[BibTex]