TY - JOUR A1 - Feis, Joshua A1 - Beutel, Dominik A1 - Köpfler, Julian A1 - Santiago, Xavier Garcia A1 - Rockstuhl, Carsten A1 - Wegener, Martin A1 - Fernandez-Corbaton, Ivan T1 - Helicity-Preserving Optical Cavity Modes for Enhanced Sensing of Chiral Molecules JF - Phys. Rev. Lett. Y1 - 2020 U6 - https://doi.org/10.1103/PhysRevLett.124.033201 VL - 124 SP - 033201 ER - TY - JOUR A1 - Scott, Philip A1 - Santiago, Xavier Garcia A1 - Beutel, Dominik A1 - Rockstuhl, Carsten A1 - Wegener, Martin A1 - Fernandez-Corbaton, Ivan T1 - On enhanced sensing of chiral molecules in optical cavities JF - Appl. Phys. Rev. Y1 - 2020 U6 - https://doi.org/10.1063/5.0025006 VL - 7 SP - 041413 ER - TY - JOUR A1 - Gamrath, Gerald A1 - Gleixner, Ambros A1 - Koch, Thorsten A1 - Miltenberger, Matthias A1 - Kniasew, Dimitri A1 - Schlögel, Dominik A1 - Martin, Alexander A1 - Weninger, Dieter T1 - Tackling Industrial-Scale Supply Chain Problems by Mixed-Integer Programming JF - Journal of Computational Mathematics N2 - The modeling flexibility and the optimality guarantees provided by mixed-integer programming greatly aid the design of robust and future-proof decision support systems. The complexity of industrial-scale supply chain optimization, however, often poses limits to the application of general mixed-integer programming solvers. In this paper we describe algorithmic innovations that help to ensure that MIP solver performance matches the complexity of the large supply chain problems and tight time limits encountered in practice. Our computational evaluation is based on a diverse set, modeling real-world scenarios supplied by our industry partner SAP. Y1 - 2019 U6 - https://doi.org/10.4208/jcm.1905-m2019-0055 VL - 37 SP - 866 EP - 888 ER - TY - JOUR A1 - Caruso, Fabio A1 - Sentef, Michael A1 - Attaccalite, Claudio A1 - Bonitz, Michael A1 - Draxl, Claudia A1 - De Giovannini, Umberto A1 - Eckstein, Martin A1 - Ernstorfer, Ralph A1 - Fechner, Michael A1 - Grüning, Myrta A1 - Hübener, Hannes A1 - Joost, Jan-Philip A1 - Juraschek, Dominik A1 - Karrasch, Christoph A1 - Marvin Kennes, Dante A1 - Latini, Simone A1 - Lu, I-Te A1 - Neufeld, Ofer A1 - Perfetto, Enrico A1 - Rettig, Laurenz A1 - Rodrigues Pelá, Ronaldo A1 - Rubio, Angel A1 - Rudzinski, Joseph A1 - Ruggenthaler, Michael A1 - Sangalli, Davide A1 - Schüler, Michael A1 - Shallcross, Samuel A1 - Sharma, Sangeeta A1 - Stefanucci, Gianluca A1 - Werner, Philipp T1 - The 2025 Roadmap to Ultrafast Dynamics: Frontiers of Theoretical and Computational Modelling JF - J. Phys. Mater. Y1 - 2026 U6 - https://doi.org/10.1088/2515-7639/ae1165 VL - 9 IS - 1 ER - TY - CHAP A1 - Zink, Christof A1 - Ekterai, Michael A1 - Martin, Dominik A1 - Clemens, William A1 - Maennel, Angela A1 - Mundinger, Konrad A1 - Richter, Lorenz A1 - Crump, Paul A1 - Knigge, Andrea T1 - Deep-learning-based visual inspection of facets and p-sides for efficient quality control of diode lasers T2 - High-Power Diode Laser Technology XXI N2 - The optical inspection of the surfaces of diode lasers, especially the p-sides and facets, is an essential part of the quality control in the laser fabrication procedure. With reliable, fast, and flexible optical inspection processes, it is possible to identify and eliminate defects, accelerate device selection, reduce production costs, and shorten the cycle time for product development. Due to a vast range of rapidly changing designs, structures, and coatings, however, it is impossible to realize a practical inspection with conventional software. In this work, we therefore suggest a deep learning based defect detection algorithm that builds on a Faster Regional Convolutional Neural Network (Faster R-CNN) as a core component. While for related, more general object detection problems, the application of such models is straightforward, it turns out that our task exhibits some additional challenges. On the one hand, a sophisticated pre- and postprocessing of the data has to be deployed to make the application of the deep learning model feasible. On the other hand, we find that creating labeled training data is not a trivial task in our scenario, and one has to be extra careful with model evaluation. We can demonstrate in multiple empirical assessments that our algorithm can detect defects in diode lasers accurately and reliably in most cases. We analyze the results of our production-ready pipeline in detail, discuss its limitations and provide some proposals for further improvements. Y1 - 2023 U6 - https://doi.org/10.1117/12.2648691 VL - 12403 SP - 94 EP - 112 PB - SPIE ER - TY - GEN A1 - Gamrath, Gerald A1 - Gleixner, Ambros A1 - Koch, Thorsten A1 - Miltenberger, Matthias A1 - Kniasew, Dimitri A1 - Schlögel, Dominik A1 - Martin, Alexander A1 - Weninger, Dieter T1 - Tackling Industrial-Scale Supply Chain Problems by Mixed-Integer Programming N2 - SAP's decision support systems for optimized supply network planning rely on mixed-integer programming as the core engine to compute optimal or near-optimal solutions. The modeling flexibility and the optimality guarantees provided by mixed-integer programming greatly aid the design of a robust and future-proof decision support system for a large and diverse customer base. In this paper we describe our coordinated efforts to ensure that the performance of the underlying solution algorithms matches the complexity of the large supply chain problems and tight time limits encountered in practice. T3 - ZIB-Report - 16-45 KW - supply chain management, supply network optimization, mixed-integer linear programming, primal heuristics, numerical stability, large-scale optimization Y1 - 2016 U6 - http://nbn-resolving.de/urn/resolver.pl?urn:nbn:de:0297-zib-61107 SN - 1438-0064 ER -