TY - GEN A1 - Villani, Paolo A1 - Weiser, Martin T1 - Adaptive Gaussian process regression for inverse problems. ALGORITMY 2024 N2 - This submission contains the code used for the proceedings paper for ALGORITMY 2024, concerning surrogate model-based inverse problems. Version 2 modified plot appearances and fixed some minor typos. Y1 - 2024 ER - TY - JOUR A1 - Ernst, Marco A1 - Asselineau, Charles-Alexis A1 - Tillmann, Peter A1 - Jäger, Klaus A1 - Becker, Christiane T1 - Modelling bifacial irradiance – Step-by-step comparison and validation of view factor and ray tracing models JF - Appl. Energy Y1 - 2024 U6 - https://doi.org/10.1016/j.apenergy.2024.123574 SN - 0306-2619 VL - 369 SP - 123574 PB - Elsevier BV ER - TY - GEN A1 - Sekulic, Ivan A1 - Schneider, Philipp-Immanuel A1 - Hammerschmidt, Martin A1 - Burger, Sven T1 - Machine-learning driven design of metasurfaces: learn the physics and not the objective function T2 - Proc. SPIE Y1 - 2024 U6 - https://doi.org/10.1117/12.3022119 VL - PC13017 SP - PC130170X ER - TY - GEN A1 - Bopp, Julian M. A1 - Plock, Matthias A1 - Turan, Tim A1 - Pieplow, Gregor A1 - Burger, Sven A1 - Schröder, Tim T1 - ‘Sawfish’ Photonic Crystal Cavity for Near-Unity Emitter-to-Fiber Interfacing in Quantum Network Applications (Advanced Optical Materials 13/2024) T2 - Adv. Optical Mater. Y1 - 2024 U6 - https://doi.org/10.1002/adom.202470046 VL - 12 SP - 2470046 ER - TY - JOUR A1 - Klus, Stefan A1 - Djurdjevac Conrad, Natasa T1 - Dynamical systems and complex networks: A Koopman operator perspective JF - Journal of Physics: Complexity N2 - The Koopman operator has entered and transformed many research areas over the last years. Although the underlying concept–representing highly nonlinear dynamical systems by infinite-dimensional linear operators–has been known for a long time, the availability of large data sets and efficient machine learning algorithms for estimating the Koopman operator from data make this framework extremely powerful and popular. Koopman operator theory allows us to gain insights into the characteristic global properties of a system without requiring detailed mathematical models. We will show how these methods can also be used to analyze complex networks and highlight relationships between Koopman operators and graph Laplacians. Y1 - 2024 U6 - https://doi.org/10.1088/2632-072X/ad9e60 VL - 5 IS - 4 SP - 041001 PB - IOP Publishing ER - TY - JOUR A1 - Krüger, Jan A1 - Manley, Phillip A1 - Bergmann, Detlef A1 - Köning, Rainer A1 - Bodermann, Bernd A1 - Eder, Christian A1 - Heinrich, Andreas A1 - Schneider, Philipp-Immanuel A1 - Hammerschmidt, Martin A1 - Zschiedrich, Lin A1 - Manske, Eberhard T1 - Introduction and application of a new approach for model-based optical bidirectional measurements JF - Meas. Sci. Technol. Y1 - 2024 U6 - https://doi.org/10.1088/1361-6501/ad4b53 VL - 35 SP - 085014 ER - TY - JOUR A1 - Manley, Phillip A1 - Hammerschmidt, Martin A1 - Zschiedrich, Lin A1 - Jäger, Klaus A1 - Becker, Christiane A1 - Burger, Sven T1 - Modelling luminescent coupling in multi-junction solar cells: perovskite silicon tandem case study JF - Proc. SPIE Y1 - 2024 U6 - https://doi.org/10.1117/12.3023941 VL - 13014 SP - 1301404 ER - TY - CHAP A1 - Schaible, Jonas A1 - Yoo, Danbi A1 - Winarto, Hanifa A1 - Miaskiewicz, Alexandra A1 - Albrecht, Steve A1 - Wessels, Andreas A1 - Bläsi, Benedikt A1 - Burger, Sven A1 - Jaeger, Klaus A1 - Becker, Christiane T1 - On aesthetical appearance of colored perovskite solar modules T2 - Proc. SPIE Y1 - 2024 U6 - https://doi.org/10.1117/12.3017427 VL - PC13014 SP - PC1301406 PB - SPIE ER - TY - CHAP A1 - Becker, Christiane A1 - Bläsi, Benedikt A1 - Burger, Sven A1 - Ehrler, Bruno A1 - Gordon, Ivan A1 - Isabella, Olindo A1 - Jäger, Klaus A1 - Jost, Marko A1 - Manley, Phillip A1 - Muñoz, Delfina A1 - Paetzold, Ulrich W. A1 - Sai, Hitoshi A1 - Yablonovitch, Eli T1 - Optical challenges for terawatt-scale photovoltaics T2 - Proc. SPIE Y1 - 2024 U6 - https://doi.org/10.1117/12.3016415 VL - PC13014 SP - PC1301401 PB - SPIE ER - TY - CHAP A1 - Semler, Phillip A1 - Weiser, Martin ED - Fackeldey, Konstantin ED - Kannan, Aswin ED - Pokutta, Sebastian ED - Sharma, Kartikey ED - Walter, Daniel ED - Walther, Andrea ED - Weiser, Martin T1 - Adaptive gradient-enhanced Gaussian process surrogates for inverse problems T2 - Mathematical Optimization for Machine Learning: Proceedings of the MATH+ Thematic Einstein Semester 2023 N2 - Generating simulated training data needed for constructing sufficiently accurate surrogate models to be used for efficient optimization or parameter identification can incur a huge computational effort in the offline phase. We consider a fully adaptive greedy approach to the computational design of experiments problem using gradient-enhanced Gaussian process regression as surrogates. Designs are incrementally defined by solving an optimization problem for accuracy given a certain computational budget. We address not only the choice of evaluation points but also of required simulation accuracy, both of values and gradients of the forward model. Numerical results show a significant reduction of the computational effort compared to just position-adaptive and static designs as well as a clear benefit of including gradient information into the surrogate training. Y1 - 2025 U6 - https://doi.org/10.1515/9783111376776-005 SP - 59 EP - 78 PB - De Gruyter ER - TY - JOUR A1 - Djurdjevac Conrad, Natasa A1 - Quang Vu, Nhu A1 - Nagel, Soeren T1 - Co-evolving networks for opinion and social dynamics in agent-based models JF - Chaos: An Interdisciplinary Journal of Nonlinear Science N2 - The rise of digital social media has strengthened the coevolution of public opinions and social interactions, that shape social structures and collective outcomes in increasingly complex ways. Existing literature often explores this interplay as a one-directional influence, focusing on how opinions determine social ties within adaptive networks. However, this perspective overlooks the intrinsic dynamics driving social interactions, which can significantly influence how opinions form and evolve. In this work, we address this gap, by introducing the co-evolving opinion and social dynamics using stochastic agent-based models. Agents' mobility in a social space is governed by both their social and opinion similarity with others. Similarly, the dynamics of opinion formation is driven by the opinions of agents in their social vicinity. We analyze the underlying social and opinion interaction networks and explore the mechanisms influencing the appearance of emerging phenomena, like echo chambers and opinion consensus. To illustrate the model's potential for real-world analysis, we apply it to General Social Survey data on political identity and public opinion regarding governmental issues. Our findings highlight the model's strength in capturing the coevolution of social connections and individual opinions over time. Y1 - 2024 U6 - https://doi.org/10.1063/5.0226054 SN - 1054-1500 VL - 34 IS - 9 ER -