@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} } @inproceedings{PlockBurgerSchneider2023, author = {Plock, Matthias and Burger, Sven and Schneider, Philipp-Immanuel}, title = {Efficient reconstruction of model parameters using Bayesian target-vector optimization}, volume = {PC12619}, booktitle = {Proc. SPIE}, doi = {10.1117/12.2673590}, pages = {PC1261905}, year = {2023}, 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} } @misc{SekulicSchaibleMuelleretal.2025, author = {Sekulic, Ivan and Schaible, Jonas and M{\"u}ller, Gabriel and Plock, Matthias and Burger, Sven and Martinez-Lahuerta, Victor J. and Gaaloul, Naceur and Schneider, Philipp-Immanuel}, title = {Data publication for Physics-informed Bayesian optimization of expensive-to-evaluate black-box functions}, journal = {Zenodo}, doi = {10.5281/zenodo.16751507}, year = {2025}, 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} } @article{PlockHammerschmidtBurgeretal.2023, author = {Plock, Matthias and Hammerschmidt, Martin and Burger, Sven and Schneider, Philipp-Immanuel and Sch{\"u}tte, Christof}, title = {Impact Study of Numerical Discretization Accuracy on Parameter Reconstructions and Model Parameter Distributions}, volume = {60}, journal = {Metrologia}, arxiv = {http://arxiv.org/abs/2305.02663}, doi = {10.1088/1681-7575/ace4cd}, pages = {054001}, year = {2023}, abstract = {In optical nano metrology numerical models are used widely for parameter reconstructions. Using the Bayesian target vector optimization method we fit a finite element numerical model to a Grazing Incidence x-ray fluorescence data set in order to obtain the geometrical parameters of a nano structured line grating. Gaussian process, stochastic machine learning surrogate models, were trained during the reconstruction and afterwards sampled with a Markov chain Monte Carlo sampler to determine the distribution of the reconstructed model parameters. The numerical discretization parameters of the used finite element model impact the numerical discretization error of the forward model. We investigated the impact of the polynomial order of the finite element ansatz functions on the reconstructed parameters as well as on the model parameter distributions. We showed that such a convergence study allows to determine numerical parameters which allows for efficient and accurate reconstruction results.}, language = {en} } @inproceedings{HammerschmidtPlockBurgeretal.2025, author = {Hammerschmidt, Martin and Plock, Matthias and Burger, Sven and Truong, Vinh and Soltwisch, Victor and Schneider, Philipp-Immanuel}, title = {Machine learning approach for full Bayesian parameter reconstruction}, volume = {13568}, booktitle = {Proc. SPIE}, doi = {10.1117/12.3062268}, pages = {1356806}, year = {2025}, language = {en} } @article{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 = {High Purcell enhancement in all-TMDC nanobeam resonator designs with active monolayers for nanolasers}, volume = {112}, journal = {Phys. Rev. B}, arxiv = {http://arxiv.org/abs/2508.05333}, doi = {10.1103/nxh9-dhvx}, pages = {235410}, year = {2025}, 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} } @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{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} } @article{RickertBetzPlocketal.2023, author = {Rickert, Lucas and Betz, Fridtjof and Plock, Matthias and Burger, Sven and Heindel, Tobias}, title = {High-performance designs for fiber-pigtailed quantum-light sources based on quantum dots in electrically-controlled circular Bragg gratings}, volume = {31}, journal = {Opt. Express}, arxiv = {http://arxiv.org/abs/2212.04883}, doi = {10.1364/OE.486060}, pages = {14750}, year = {2023}, language = {en} } @misc{RickertBetzPlocketal.2022, author = {Rickert, Lucas and Betz, Fridtjof and Plock, Matthias and Burger, Sven and Heindel, Tobias}, title = {Data publication for "High-performance designs for fiber-pigtailed quantum-light sources based on quantum dots in electrically-controlled circular Bragg gratings"}, journal = {Zenodo}, doi = {10.5281/zenodo.7360516}, pages = {7360516}, year = {2022}, 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} } @article{PlockBinkowskiZschiedrichetal.2024, author = {Plock, Matthias and Binkowski, Felix and Zschiedrich, Lin and Schneider, Phillip-Immanuel and Burger, Sven}, title = {Fabrication uncertainty guided design optimization of a photonic crystal cavity by using Gaussian processes}, volume = {41}, journal = {J. Opt. Soc. Am. B}, arxiv = {http://arxiv.org/abs/2402.09420}, doi = {10.1364/JOSAB.505767}, pages = {850}, 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{PlockAndrleBurgeretal.2022, author = {Plock, Matthias and Andrle, Kas and Burger, Sven and Schneider, Philipp-Immanuel}, title = {Bayesian Target-Vector Optimization for Efficient Parameter Reconstruction}, volume = {5}, journal = {Adv. Theory Simul.}, arxiv = {http://arxiv.org/abs/2202.11559}, doi = {10.1002/adts.202200112}, pages = {2200112}, year = {2022}, language = {en} }