@article{51737, author = {{Kullmer, Gunter and Weiß, Deborah and Schramm, Britta}}, issn = {{0013-7944}}, journal = {{Engineering Fracture Mechanics}}, keywords = {{Mechanical Engineering, Mechanics of Materials, General Materials Science}}, publisher = {{Elsevier BV}}, title = {{{An alternative and robust formulation of the fatigue crack growth rate curve for long cracks}}}, doi = {{10.1016/j.engfracmech.2023.109826}}, volume = {{296}}, year = {{2024}}, } @inbook{51114, author = {{Meyer zu Hörste-Bührer, Raphaela}}, booktitle = {{Ethik der Zeit - Zeiten der Ethik}}, editor = {{Meyer zu Hörste-Bührer, Raphaela J. and Zimmermann, Ruben and Erbele-Küster, Dorothea and Roth, Michael and Volp, Ulrich}}, pages = {{31--46}}, title = {{{Zeit für das Leben - Notwendigkeit und Probleme einer zeitsensiblen Ethik}}}, volume = {{14}}, year = {{2024}}, } @book{51113, editor = {{Meyer zu Hörste-Bührer, Raphaela J. and Zimmermann, Ruben and Erbele-Küster, Dorothea and Roth, Michael and Volp, Ulrich}}, pages = {{363}}, publisher = {{Mohr Siebeck}}, title = {{{Ethik der Zeit - Zeiten der Ethik}}}, volume = {{14}}, year = {{2024}}, } @inbook{51745, author = {{Schulz, Christian}}, booktitle = {{Digitale Schriftlichkeit – Progammieren, Prozessieren und Codieren von Schrift}}, editor = {{Bartelmus, Martin and Nebrig, Alexander}}, publisher = {{transcript }}, title = {{{Vernakulärer Code oder die Geister, die der Algorithmus rief - digitale Schriftlichkeit im Kontext von sozialen Medienplattformen}}}, year = {{2024}}, } @inbook{51748, author = {{Schulz, Christian}}, booktitle = {{Populäre Artikulationen – Artikulationen des Populären}}, editor = {{Koch, Günter and Rottgeri , André }}, publisher = {{Schüren}}, title = {{{In Likes We Trust oder die unmögliche Möglichkeit vom Like als Gabe zu sprechen}}}, year = {{2024}}, } @inbook{51746, author = {{Schulz, Christian}}, booktitle = {{Die Fotografie und ihre Institutionen. Von der Lehrsammlung zum Bundesinstitut }}, editor = {{Schürmann, Anja and Yacavone, Kathrin }}, publisher = {{Reimer Verlag}}, title = {{{Vom foto-sozialen Graph zum Story-Format: Über die Institutionalisierung sozialmedialer Infrastruktur aus dem Geiste der Fotografie}}}, year = {{2024}}, } @inbook{51747, author = {{de Gruisbourne, Birte and Schulz, Christian}}, booktitle = {{Inquiring healing across screen cultures: Recuperating narratives, mediums, and creativities}}, editor = {{Çiçek, Özgür and Savaş, Özlem}}, publisher = {{Routledge}}, title = {{{A Healing Media System of Care - Cancer Diaries and Social Media}}}, year = {{2024}}, } @book{51764, editor = {{Adelmann, Ralf and Matzner, Tobias and Miggelbrink, Monique and Schulz, Christian }}, publisher = {{MediaRep}}, title = {{{Filter – Medienwissenschaftliche Symposien der DFG}}}, year = {{2024}}, } @inbook{51790, author = {{Richter, Susanne}}, booktitle = {{Inklusiver Kinderschutz – Anforderungen, Herausforderungen, Perspektiven}}, editor = {{Kieslinger, Daniel and Owsianowski, Judith}}, isbn = {{978-3-7841-3666-0}}, publisher = {{Lambertus}}, title = {{{Herausforderungen in der inklusiven Mädchenarbeit: Begleitforschung der „Inklusiven anonymen Zuflucht“ des Mädchenhaus Bielefeld e.V.}}}, year = {{2024}}, } @article{40171, abstract = {{We present a convolutional framework which significantly reduces the complexity and thus, the computational effort for distributed reinforcement learning control of dynamical systems governed by partial differential equations (PDEs). Exploiting translational equivariances, the high-dimensional distributed control problem can be transformed into a multi-agent control problem with many identical, uncoupled agents. Furthermore, using the fact that information is transported with finite velocity in many cases, the dimension of the agents’ environment can be drastically reduced using a convolution operation over the state space of the PDE, by which we effectively tackle the curse of dimensionality otherwise present in deep reinforcement learning. In this setting, the complexity can be flexibly adjusted via the kernel width or by using a stride greater than one (meaning that we do not place an actuator at each sensor location). Moreover, scaling from smaller to larger domains – or the transfer between different domains – becomes a straightforward task requiring little effort. We demonstrate the performance of the proposed framework using several PDE examples with increasing complexity, where stabilization is achieved by training a low-dimensional deep deterministic policy gradient agent using minimal computing resources.}}, author = {{Peitz, Sebastian and Stenner, Jan and Chidananda, Vikas and Wallscheid, Oliver and Brunton, Steven L. and Taira, Kunihiko}}, journal = {{Physica D: Nonlinear Phenomena}}, pages = {{134096}}, publisher = {{Elsevier}}, title = {{{Distributed Control of Partial Differential Equations Using Convolutional Reinforcement Learning}}}, doi = {{10.1016/j.physd.2024.134096}}, volume = {{461}}, year = {{2024}}, }