@misc{17740, author = {{Peckhaus, Volker}}, booktitle = {{The Stanford Encyclopedia of Philosophy, first published Sep 4, 2009, substantive revision Feb 2, 2024}}, editor = {{Zalta, Edward N.. and Nodelman, Uri}}, title = {{{Leibniz’s Influence on 19th Century Logic}}}, year = {{2024}}, } @article{51519, author = {{Cui, Tie Jun and Zhang, Shuang and Alu, Andrea and Wegener, Martin and Pendry, John and Luo, Jie and Lai, Yun and Wang, Zuojia and Lin, Xiao and Chen, Hongsheng and Chen, Ping and Wu, Rui-Xin and Yin, Yuhang and Zhao, Pengfei and Chen, Huanyang and Li, Yue and Zhou, Ziheng and Engheta, Nader and Asadchy, V. S. and Simovski, Constantin and Tretyakov, Sergei A and Yang, Biao and Campbell, Sawyer D. and Hao, Yang and Werner, Douglas H and Sun, Shulin and Zhou, Lei and Xu, Su and Sun, Hong-Bo and Zhou, Zhou and Li, Zile and Zheng, Guoxing and Chen, Xianzhong and Li, Tao and Zhu, Shi-Ning and Zhou, Junxiao and Zhao, Junxiang and Liu, Zhaowei and Zhang, Yuchao and Zhang, Qiming and Gu, Min and Xiao, Shumin and Liu, Yongmin and Zhang, Xiaoyu and Tang, Yutao and Li, Guixin and Zentgraf, Thomas and Koshelev, Kirill and Kivshar, Yuri S. and Li, Xin and Badloe, Trevon and Huang, Lingling and Rho, Junsuk and Wang, Shuming and Tsai, Din Ping and Bykov, A. Yu. and Krasavin, Alexey V and Zayats, Anatoly V and McDonnell, Cormac and Ellenbogen, Tal and Luo, Xiangang and Pu, Mingbo and Garcia-Vidal, Francisco J and Liu, Liangliang and Li, Zhuo and Tang, Wenxuan and Ma, Hui Feng and Zhang, Jingjing and Luo, Yu and Zhang, Xuanru and Zhang, Hao Chi and He, Pei Hang and Zhang, Le Peng and Wan, Xiang and Wu, Haotian and Liu, Shuo and Jiang, Wei Xiang and Zhang, Xin Ge and Qiu, Chengwei and Ma, Qian and Liu, Che and Li, Long and Han, Jiaqi and Li, Lianlin and Cotrufo, Michele and Caloz, Christophe and Deck-Léger, Z.-L. and Bahrami, A. and Céspedes, O. and Galiffi, Emanuele and Huidobro, P. A. and Cheng, Qiang and Dai, Jun Yan and Ke, Jun Cheng and Zhang, Lei and Galdi, Vincenzo and Di Renzo, Marco}}, issn = {{2515-7647}}, journal = {{Journal of Physics: Photonics}}, keywords = {{Electrical and Electronic Engineering, Atomic and Molecular Physics, and Optics, Electronic, Optical and Magnetic Materials}}, publisher = {{IOP Publishing}}, title = {{{Roadmap on electromagnetic metamaterials and metasurfaces}}}, doi = {{10.1088/2515-7647/ad1a3b}}, year = {{2024}}, } @article{46019, abstract = {{We derive efficient algorithms to compute weakly Pareto optimal solutions for smooth, convex and unconstrained multiobjective optimization problems in general Hilbert spaces. To this end, we define a novel inertial gradient-like dynamical system in the multiobjective setting, which trajectories converge weakly to Pareto optimal solutions. Discretization of this system yields an inertial multiobjective algorithm which generates sequences that converge weakly to Pareto optimal solutions. We employ Nesterov acceleration to define an algorithm with an improved convergence rate compared to the plain multiobjective steepest descent method (Algorithm 1). A further improvement in terms of efficiency is achieved by avoiding the solution of a quadratic subproblem to compute a common step direction for all objective functions, which is usually required in first-order methods. Using a different discretization of our inertial gradient-like dynamical system, we obtain an accelerated multiobjective gradient method that does not require the solution of a subproblem in each step (Algorithm 2). While this algorithm does not converge in general, it yields good results on test problems while being faster than standard steepest descent.}}, author = {{Sonntag, Konstantin and Peitz, Sebastian}}, journal = {{Journal of Optimization Theory and Applications}}, publisher = {{Springer}}, title = {{{Fast Multiobjective Gradient Methods with Nesterov Acceleration via Inertial Gradient-Like Systems}}}, doi = {{10.1007/s10957-024-02389-3}}, year = {{2024}}, } @unpublished{51334, abstract = {{The efficient optimization method for locally Lipschitz continuous multiobjective optimization problems from [1] is extended from finite-dimensional problems to general Hilbert spaces. The method iteratively computes Pareto critical points, where in each iteration, an approximation of the subdifferential is computed in an efficient manner and then used to compute a common descent direction for all objective functions. To prove convergence, we present some new optimality results for nonsmooth multiobjective optimization problems in Hilbert spaces. Using these, we can show that every accumulation point of the sequence generated by our algorithm is Pareto critical under common assumptions. Computational efficiency for finding Pareto critical points is numerically demonstrated for multiobjective optimal control of an obstacle problem.}}, author = {{Sonntag, Konstantin and Gebken, Bennet and Müller, Georg and Peitz, Sebastian and Volkwein, Stefan}}, booktitle = {{arXiv:2402.06376}}, title = {{{A Descent Method for Nonsmooth Multiobjective Optimization in Hilbert Spaces}}}, 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}}, } @article{52357, author = {{Beimdiek, Janis and Schmid, Hans-Joachim}}, issn = {{2073-4433}}, journal = {{Atmosphere}}, keywords = {{surrogate aerosols, indoor air cleaners, ultra-fine particles, COVID-19, test method, field experiments: clean air delivery rate}}, number = {{3}}, publisher = {{Multidisciplinary Digital Publishing Institute (MDPI)}}, title = {{{Evaluation of Surrogate Aerosol Experiments to Predict Spreading and Removal of Virus-Laden Aerosols}}}, doi = {{ 10.3390/atmos15030305}}, volume = {{15}}, year = {{2024}}, } @article{51122, author = {{Al-Lami, Abbas J.S. and Kenig, Eugeny Y.}}, issn = {{2214-157X}}, journal = {{Case Studies in Thermal Engineering}}, keywords = {{Fluid Flow and Transfer Processes, Engineering (miscellaneous)}}, publisher = {{Elsevier BV}}, title = {{{New pressure drop and heat transfer correlations for turbulent forced convection in internally channeled tube heat exchanger ducts}}}, doi = {{10.1016/j.csite.2024.103993}}, year = {{2024}}, } @misc{52465, author = {{Breckner, Anne}}, title = {{{All you need is love… Gedanken für das neue Jahr}}}, year = {{2024}}, } @article{52503, author = {{Kundisch, Heike and Kremer, H.-Hugo and Otto, Franziska}}, journal = {{QfI - Qualifizierung für Inklusion Online-Zeitschrift zur Forschung über Aus-, Fort- und Weiterbildung pädagogischer Fachkräfte}}, number = {{3}}, title = {{{Selbstinszenierungspraktiken als Weg zu Stärkenorientierung, Selbstbestimmung und Teilhabe – eine Kollegiale Weiterbildung für multiprofessionelle Akteursgruppen im (inklusiven) Übergang Schule-Beruf}}}, doi = {{ https://doi.org/10.21248/qfi.136}}, volume = {{5}}, year = {{2024}}, } @inbook{52538, abstract = {{Twitter ist jetzt X und befindet sich auf dem absteigenden Ast. Auf diesem Ast sitzt Facebook bereits seit längerem. Der Kurzvideodienst Vine, Vorgänger von TikTok, ist Geschichte. Und auch bei Google klingeln die Alarmglocken angesichts der „neuen“ Konkurrenz durch Microsoft und ChatGPT. Umso dringlicher wird also die Historisierung der „sozialen Medien“, das heißt, sie in ihren historischen Kontext einzuordnen und ihren Mythos zu entzaubern. Dabei wartet das Vorhaben mit einer doppelten Herausforderung auf: Erstens, dass es sich bei den Unternehmen der Branche und zweitens auch bei den dort gebildeten Gemeinschaften um recht flüchtige, wandelhaften Gestalten handelt. Scheitern und Wandel ist Teil der „sozialen Medien“ – und sei es nur in der schnellen Abfolge der Moden des „nächsten großen Dings“. Dementsprechend versucht dieser Beitrag mit einem systematischeren Ansatz als dem der gesellschaftlichen Selbstbeschreibung, die Entwicklung des sozio-digitalen Phänomens „soziale Medien“ in dessen Zeitkontext einzuordnen.}}, author = {{Schmitt, Martin}}, booktitle = {{Soziale Medien – wie sie wurden, was sie sind}}, keywords = {{Digitalgeschichte, Soziale Medien, Technikgeschichte, Wirtschaftsgeschichte, Digitalisierung, Twitter, Facebook, Meta}}, publisher = {{Bundeszentrale für politische Bildung}}, title = {{{Alles geht? Die jüngste Geschichte der „sozialen Medien“. Zwischen Wirtschaft und Gemeinschaft}}}, year = {{2024}}, } @article{33461, abstract = {{Data-driven models for nonlinear dynamical systems based on approximating the underlying Koopman operator or generator have proven to be successful tools for forecasting, feature learning, state estimation, and control. It has become well known that the Koopman generators for control-affine systems also have affine dependence on the input, leading to convenient finite-dimensional bilinear approximations of the dynamics. Yet there are still two main obstacles that limit the scope of current approaches for approximating the Koopman generators of systems with actuation. First, the performance of existing methods depends heavily on the choice of basis functions over which the Koopman generator is to be approximated; and there is currently no universal way to choose them for systems that are not measure preserving. Secondly, if we do not observe the full state, we may not gain access to a sufficiently rich collection of such functions to describe the dynamics. This is because the commonly used method of forming time-delayed observables fails when there is actuation. To remedy these issues, we write the dynamics of observables governed by the Koopman generator as a bilinear hidden Markov model, and determine the model parameters using the expectation-maximization (EM) algorithm. The E-step involves a standard Kalman filter and smoother, while the M-step resembles control-affine dynamic mode decomposition for the generator. We demonstrate the performance of this method on three examples, including recovery of a finite-dimensional Koopman-invariant subspace for an actuated system with a slow manifold; estimation of Koopman eigenfunctions for the unforced Duffing equation; and model-predictive control of a fluidic pinball system based only on noisy observations of lift and drag.}}, author = {{Otto, Samuel E. and Peitz, Sebastian and Rowley, Clarence W.}}, journal = {{SIAM Journal on Applied Dynamical Systems}}, number = {{1}}, pages = {{885--923}}, publisher = {{SIAM}}, title = {{{Learning Bilinear Models of Actuated Koopman Generators from Partially-Observed Trajectories}}}, doi = {{10.1137/22M1523601}}, volume = {{23}}, year = {{2024}}, } @article{52652, author = {{Herdramm, Henrike}}, journal = {{BloKK. Der Blog des Zentrums für Komparative Theologie und Kulturwissenschaften}}, keywords = {{Ideologiekritik, Tiertheologie, Tierethik}}, title = {{{Das Potenzial von Ideologiekritik im Religionsunterricht}}}, year = {{2024}}, } @article{52702, abstract = {{The editorial introduces the special issue Knowledge by Design in Education: Key challenges and experiences from research practice, posing key questions, offering an insight into ongoing discussions, and presenting an overview of the included articles.}}, author = {{Brase, Alexa Kristin and Jenert, Tobias}}, issn = {{2511-0667}}, journal = {{EDeR. Educational Design Research}}, number = {{1}}, publisher = {{Staats- und Universitatsbibliothek Hamburg Carl von Ossietzky}}, title = {{{Knowledge by Design in Education}}}, doi = {{10.15460/eder.8.1.2213}}, volume = {{8}}, year = {{2024}}, } @article{52726, abstract = {{Heteroclinic structures organize global features of dynamical systems. We analyse whether heteroclinic structures can arise in network dynamics with higher-order interactions which describe the nonlinear interactions between three or more units. We find that while commonly analysed model equations such as network dynamics on undirected hypergraphs may be useful to describe local dynamics such as cluster synchronization, they give rise to obstructions that allow to design of heteroclinic structures in phase space. By contrast, directed hypergraphs break the homogeneity and lead to vector fields that support heteroclinic structures.}}, author = {{Bick, Christian and von der Gracht, Sören}}, issn = {{2051-1329}}, journal = {{Journal of Complex Networks}}, keywords = {{Applied Mathematics, Computational Mathematics, Control and Optimization, Management Science and Operations Research, Computer Networks and Communications}}, number = {{2}}, publisher = {{Oxford University Press (OUP)}}, title = {{{Heteroclinic dynamics in network dynamical systems with higher-order interactions}}}, doi = {{10.1093/comnet/cnae009}}, volume = {{12}}, year = {{2024}}, } @article{52372, abstract = {{Due to the hydrolytic instability of LiPF6 in carbonate-based solvents, HF is a typical impurity in Li-ion battery electrolytes. HF significantly influences the performance of Li-ion batteries, for example by impacting the formation of the solid electrolyte interphase at the anode and by affecting transition metal dissolution at the cathode. Additionally, HF complicates studying fundamental interfacial electrochemistry of Li-ion battery electrolytes, such as direct anion reduction, because it is electrocatalytically relatively unstable, resulting in LiF passivation layers. Methods to selectively remove ppm levels of HF from LiPF6-containing carbonate-based electrolytes are limited. We introduce and benchmark a simple yet efficient electrochemical in situ method to selectively remove ppm amounts of HF from LiPF6-containing carbonate-based electrolytes. The basic idea is the application of a suitable potential to a high surface-area metallic electrode upon which only HF reacts (electrocatalytically) while all other electrolyte components are unaffected under the respective conditions.}}, author = {{Ge, Xiaokun and Huck, Marten and Kuhlmann, Andreas and Tiemann, Michael and Weinberger, Christian and Xu, Xiaodan and Zhao, Zhenyu and Steinrueck, Hans-Georg}}, issn = {{0013-4651}}, journal = {{Journal of The Electrochemical Society}}, keywords = {{Materials Chemistry, Electrochemistry, Surfaces, Coatings and Films, Condensed Matter Physics, Renewable Energy, Sustainability and the Environment, Electronic, Optical and Magnetic Materials}}, pages = {{030552}}, publisher = {{The Electrochemical Society}}, title = {{{Electrochemical Removal of HF from Carbonate-based LiPF6-containing Li-ion Battery Electrolytes}}}, doi = {{10.1149/1945-7111/ad30d3}}, volume = {{171}}, year = {{2024}}, } @article{53101, abstract = {{In this work, we consider optimal control problems for mechanical systems with fixed initial and free final state and a quadratic Lagrange term. Specifically, the dynamics is described by a second order ODE containing an affine control term. Classically, Pontryagin's maximum principle gives necessary optimality conditions for the optimal control problem. For smooth problems, alternatively, a variational approach based on an augmented objective can be followed. Here, we propose a new Lagrangian approach leading to equivalent necessary optimality conditions in the form of Euler-Lagrange equations. Thus, the differential geometric structure (similar to classical Lagrangian dynamics) can be exploited in the framework of optimal control problems. In particular, the formulation enables the symplectic discretisation of the optimal control problem via variational integrators in a straightforward way.}}, author = {{Leyendecker, Sigrid and Maslovskaya, Sofya and Ober-Blöbaum, Sina and Almagro, Rodrigo T. Sato Martín de and Szemenyei, Flóra Orsolya}}, issn = {{2158-2491}}, journal = {{Journal of Computational Dynamics}}, keywords = {{Optimal control problem, Lagrangian system, Hamiltonian system, Variations, Pontryagin's maximum principle.}}, pages = {{0--0}}, publisher = {{American Institute of Mathematical Sciences (AIMS)}}, title = {{{A new Lagrangian approach to control affine systems with a quadratic Lagrange term}}}, doi = {{10.3934/jcd.2024017}}, year = {{2024}}, } @inproceedings{53106, author = {{Bußemas, Leon and Fittkau, Niklas and Gausemeier, Sandra and Trächtler, Ansgar and Rüddenklau, Nico}}, booktitle = {{VDI Mechatroniktagung Dresden 2024}}, location = {{Dresden}}, pages = {{29--34}}, publisher = {{Technische Universität Dresden}}, title = {{{LiDAR-Sensormodell basierend auf zeitabhängigem Photon Mapping}}}, year = {{2024}}, } @article{53130, author = {{Stumpe, Miriam and Dieter, Peter and Schryen, Guido and Müller, Oliver and Beverungen, Daniel}}, journal = {{Transportation Research Part A: Policy and Practice}}, title = {{{Designing taxi ridesharing systems with shared pick-up and drop-off locations: Insights from a computational study}}}, year = {{2024}}, } @article{38031, abstract = {{We consider the data-driven approximation of the Koopman operator for stochastic differential equations on reproducing kernel Hilbert spaces (RKHS). Our focus is on the estimation error if the data are collected from long-term ergodic simulations. We derive both an exact expression for the variance of the kernel cross-covariance operator, measured in the Hilbert-Schmidt norm, and probabilistic bounds for the finite-data estimation error. Moreover, we derive a bound on the prediction error of observables in the RKHS using a finite Mercer series expansion. Further, assuming Koopman-invariance of the RKHS, we provide bounds on the full approximation error. Numerical experiments using the Ornstein-Uhlenbeck process illustrate our results.}}, author = {{Philipp, Friedrich and Schaller, Manuel and Worthmann, Karl and Peitz, Sebastian and Nüske, Feliks}}, journal = {{Applied and Computational Harmonic Analysis }}, publisher = {{Springer }}, title = {{{Error bounds for kernel-based approximations of the Koopman operator}}}, doi = {{10.1016/j.acha.2024.101657}}, volume = {{71}}, year = {{2024}}, }