@inproceedings{BoppPlockTuranetal.2023, author = {Bopp, Julian M. and Plock, Matthias and Turan, Tim and Pieplow, Gregor and Burger, Sven and Schr{\"o}der, Tim}, title = {'Sawfish' Spin-Photon Interface for Near-Unity Emitter-to-Waveguide Coupling}, booktitle = {Conference on Lasers and Electro-Optics (CLEO)}, doi = {10.1364/CLEO_SI.2023.SF1O.6}, pages = {SF1O.6}, year = {2023}, language = {en} } @misc{BoppPlockTuranetal.2024, author = {Bopp, Julian M. and Plock, Matthias and Turan, Tim and Pieplow, Gregor and Burger, Sven and Schr{\"o}der, Tim}, title = {'Sawfish' Photonic Crystal Cavity for Near-Unity Emitter-to-Fiber Interfacing in Quantum Network Applications (Advanced Optical Materials 13/2024)}, volume = {12}, journal = {Adv. Optical Mater.}, doi = {10.1002/adom.202470046}, pages = {2470046}, year = {2024}, language = {en} } @misc{BinkowskiKoulasSimosBetzetal.2025, author = {Binkowski, Felix and Koulas-Simos, Aris and Betz, Fridtjof and Plock, Matthias and Sekulic, Ivan and Manley, Phillip and Hammerschmidt, Martin and Schneider, Philipp-Immanuel and Zschiedrich, Lin and Munkhbat, Battulga and Reitzenstein, Stephan and Burger, Sven}, title = {Source code and simulation results: High Purcell enhancement in all-TMDC nanobeam resonator designs with active monolayers for nanolasers}, journal = {Zenodo}, doi = {10.5281/zenodo.16533803}, year = {2025}, language = {en} } @article{BoppPlockTuranetal.2024, author = {Bopp, Julian M. and Plock, Matthias and Turan, Tim and Pieplow, Gregor and Burger, Sven and Schr{\"o}der, Tim}, title = {Sawfish Photonic Crystal Cavity for Near-Unity Emitter-to-Fiber Interfacing in Quantum Network Applications}, volume = {12}, journal = {Adv. Opt. Mater.}, arxiv = {http://arxiv.org/abs/2210.04702}, doi = {10.1002/adom.202301286}, pages = {2301286}, year = {2024}, language = {en} } @misc{PlockBinkowskiZschiedrichetal.2024, author = {Plock, Matthias and Binkowski, Felix and Zschiedrich, Lin and Schneider, Phillip-Immanuel and Burger, Sven}, title = {Research data for "Fabrication uncertainty guided design optimization of a photonic crystal cavity by using Gaussian processes"}, journal = {Zenodo}, doi = {10.5281/zenodo.8131611}, pages = {doi: 10.5281/zenodo.8131611}, year = {2024}, language = {en} } @misc{PlockAndrleBurgeretal.2022, author = {Plock, Matthias and Andrle, Kas and Burger, Sven and Schneider, Philipp-Immanuel}, title = {Research data and example scripts for the paper "Bayesian Target-Vector Optimization for Efficient Parameter Reconstruction"}, journal = {Zenodo}, doi = {10.5281/zenodo.6359594}, year = {2022}, language = {en} } @article{PlockBurgerSchneider2021, author = {Plock, Matthias and Burger, Sven and Schneider, Philipp-Immanuel}, title = {Recent advances in Bayesian optimization with applications to parameter reconstruction in optical nano-metrology}, volume = {11783}, journal = {Proc. SPIE}, arxiv = {http://arxiv.org/abs/2107.05499}, doi = {10.1117/12.2592266}, pages = {117830J}, year = {2021}, language = {en} } @inproceedings{SchneiderSekulicPlocketal.2025, author = {Schneider, Philipp-Immanuel and Sekulic, Ivan and Plock, Matthias and Hammerschmidt, Martin and Rodt, Sven and Reitzenstein, Stephan and Burger, Sven}, title = {Physics-informed Bayesian optimization of nanophotonic devices}, volume = {IPRSN}, booktitle = {Advanced Photonics Congress}, doi = {10.1364/IPRSN.2025.ITu1A.2}, pages = {ITu1A.2}, year = {2025}, language = {en} } @article{SekulicSchaibleMuelleretal.2025, author = {Sekulic, Ivan and Schaible, Jonas and M{\"u}ller, Gabriel and Plock, Matthias and Burger, Sven and Mart{\´i}nez-Lahuerta, V{\´i}ctor Jos{\´e} and Gaaloul, Naceur and Schneider, Philipp-Immanuel}, title = {Physics-informed Bayesian optimization of expensive-to-evaluate black-box functions}, volume = {6}, journal = {Mach. Learn. Sci. Technol.}, publisher = {IOP Publishing}, doi = {10.1088/2632-2153/ae1f5f}, pages = {040503}, year = {2025}, abstract = {Abstract Bayesian optimization with Gaussian process surrogates is a popular approach for optimizing expensive-to-evaluate functions in terms of time, energy, or computational resources. Typically, a Gaussian process models a scalar objective derived from observed data. However, in many real-world applications, the objective is a combination of multiple outputs from physical experiments or simulations. Converting these multidimensional observations into a single scalar can lead to information loss, slowing convergence and yielding suboptimal results. To address this, we propose to use multi-output Gaussian processes to learn the full vector of observations directly, before mapping them to the scalar objective via an inexpensive analytical function. This physics-informed approach retains more information from the underlying physical processes, improving surrogate model accuracy. As a result, the approach accelerates optimization and produces better final designs compared to standard implementations.}, language = {en} } @phdthesis{Plock2025, author = {Plock, Matthias}, title = {On Methods for Bayesian Optimization of Least Squares Problems and Optimization of Nanophotonic Devices}, doi = {10.17169/refubium-48179}, year = {2025}, language = {en} }