TY - JOUR AU - Kullmer, Gunter AU - Weiß, Deborah AU - Schramm, Britta ID - 51737 JF - Engineering Fracture Mechanics KW - Mechanical Engineering KW - Mechanics of Materials KW - General Materials Science SN - 0013-7944 TI - An alternative and robust formulation of the fatigue crack growth rate curve for long cracks VL - 296 ER - TY - CHAP AU - Meyer zu Hörste-Bührer, Raphaela ED - Meyer zu Hörste-Bührer, Raphaela J. ED - Zimmermann, Ruben ED - Erbele-Küster, Dorothea ED - Roth, Michael ED - Volp, Ulrich ID - 51114 T2 - Ethik der Zeit - Zeiten der Ethik TI - Zeit für das Leben - Notwendigkeit und Probleme einer zeitsensiblen Ethik VL - 14 ER - TY - BOOK ED - Meyer zu Hörste-Bührer, Raphaela J. ED - Zimmermann, Ruben ED - Erbele-Küster, Dorothea ED - Roth, Michael ED - Volp, Ulrich ID - 51113 TI - Ethik der Zeit - Zeiten der Ethik VL - 14 ER - TY - CHAP AU - Schulz, Christian ED - Bartelmus, Martin ED - Nebrig, Alexander ID - 51745 T2 - Digitale Schriftlichkeit – Progammieren, Prozessieren und Codieren von Schrift TI - Vernakulärer Code oder die Geister, die der Algorithmus rief - digitale Schriftlichkeit im Kontext von sozialen Medienplattformen ER - TY - CHAP AU - Schulz, Christian ED - Koch, Günter ED - Rottgeri , André ID - 51748 T2 - Populäre Artikulationen – Artikulationen des Populären TI - In Likes We Trust oder die unmögliche Möglichkeit vom Like als Gabe zu sprechen ER - TY - CHAP AU - Schulz, Christian ED - Schürmann, Anja ED - Yacavone, Kathrin ID - 51746 T2 - Die Fotografie und ihre Institutionen. Von der Lehrsammlung zum Bundesinstitut TI - Vom foto-sozialen Graph zum Story-Format: Über die Institutionalisierung sozialmedialer Infrastruktur aus dem Geiste der Fotografie ER - TY - CHAP AU - de Gruisbourne, Birte AU - Schulz, Christian ED - Çiçek, Özgür ED - Savaş, Özlem ID - 51747 T2 - Inquiring healing across screen cultures: Recuperating narratives, mediums, and creativities TI - A Healing Media System of Care - Cancer Diaries and Social Media ER - TY - BOOK ED - Adelmann, Ralf ED - Matzner, Tobias ED - Miggelbrink, Monique ED - Schulz, Christian ID - 51764 TI - Filter – Medienwissenschaftliche Symposien der DFG ER - TY - CHAP AU - Richter, Susanne ED - Kieslinger, Daniel ED - Owsianowski, Judith ID - 51790 SN - 978-3-7841-3666-0 T2 - Inklusiver Kinderschutz – Anforderungen, Herausforderungen, Perspektiven TI - Herausforderungen in der inklusiven Mädchenarbeit: Begleitforschung der „Inklusiven anonymen Zuflucht“ des Mädchenhaus Bielefeld e.V. ER - TY - JOUR AB - 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. AU - Peitz, Sebastian AU - Stenner, Jan AU - Chidananda, Vikas AU - Wallscheid, Oliver AU - Brunton, Steven L. AU - Taira, Kunihiko ID - 40171 JF - Physica D: Nonlinear Phenomena TI - Distributed Control of Partial Differential Equations Using Convolutional Reinforcement Learning VL - 461 ER -