@article{SchneiderSantiagoRockstuhletal.2017, author = {Schneider, Philipp-Immanuel and Santiago, Xavier Garcia and Rockstuhl, Carsten and Burger, Sven}, title = {Global optimization of complex optical structures using Bayesian optimization based on Gaussian processes}, volume = {10335}, journal = {Proc. SPIE}, arxiv = {http://arxiv.org/abs/1707.08479}, doi = {10.1117/12.2270609}, pages = {103350O}, year = {2017}, language = {en} } @article{TorunSchneiderHammerschmidtetal.2021, author = {Torun, Cem G{\"u}ney and Schneider, Philipp-Immanuel and Hammerschmidt, Martin and Burger, Sven and Munns, Joseph H.D. and Schr{\"o}der, Tim}, title = {Optimized diamond inverted nanocones for enhanced color center to fiber coupling}, volume = {118}, journal = {Appl. Phys. Lett.}, arxiv = {http://arxiv.org/abs/2105.13333}, doi = {10.1063/5.0050338}, pages = {234002}, year = {2021}, language = {en} } @inproceedings{SchneiderGarciaSantiagoWiegandetal.2021, author = {Schneider, Philipp-Immanuel and Garcia-Santiago, Xavier and Wiegand, Benjamin and Anton, Oliver and Krutzik, Markus and Rockstuhl, Carsten and Burger, Sven}, title = {Advances in Bayesian optimization for photonics and quantum atom optics applications}, booktitle = {OSA Advanced Photonics Congress}, doi = {10.1364/IPRSN.2021.JTh1E.2}, pages = {JTh1E.2}, year = {2021}, language = {en} } @inproceedings{MusialZolnaczSrockaetal.2020, author = {Musial, Anna and Zolnacz, Kinga and Srocka, Nicole and Kravets, Oleh and Große, Jan and Schneider, Philipp-Immanuel and Olszewski, Jacek and Poturaj, Krzystof and Wojcik, Grzegorz and Mergo, Pawel and Dybka, Kamil and Dyrkacz, Mariusz and Dlubek, Michal and Rodt, Sven and Burger, Sven and Zschiedrich, Lin and Urbanczyk, Waclaw and Sek, Grzegorz and Reitzenstein, Stephan}, title = {Stand-alone quantum dot-based single-photon source operating at telecommunication wavelengths}, booktitle = {10th International Conference on Spontaneous Coherence in Excitonic Systems ICSCE10}, pages = {39}, year = {2020}, language = {en} } @inproceedings{HammerschmidtZschiedrichSiaudinyteetal.2023, author = {Hammerschmidt, Martin and Zschiedrich, Lin and Siaudinyt{\´e}, Lauryna and Manley, Phillip and Schneider, Philipp-Immanuel and Burger, Sven}, title = {Forward simulation of coherent beams on grating structures for coherent scatterometry}, volume = {PC12619}, booktitle = {Proc. SPIE}, arxiv = {http://arxiv.org/abs/2309.06109}, doi = {10.1117/12.2673231}, pages = {PC1261907}, 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{AntonHendersonDaRosetal.2024, author = {Anton, Oliver and Henderson, Victoria A. and Da Ros, Elisa and Sekulic, Ivan and Burger, Sven and Schneider, Philipp-Immanuel and Krutzik, Markus}, title = {Review and experimental benchmarking of machine learning algorithms for efficient optimization of cold atom experiments}, volume = {5}, journal = {Mach. Learn. Sci. Technol.}, arxiv = {http://arxiv.org/abs/2312.13397}, doi = {10.1088/2632-2153/ad3cb6}, pages = {025022}, year = {2024}, 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} } @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{SekulicSchneiderHammerschmidtetal.2024, author = {Sekulic, Ivan and Schneider, Philipp-Immanuel and Hammerschmidt, Martin and Burger, Sven}, title = {Machine-learning driven design of metasurfaces: learn the physics and not the objective function}, volume = {PC13017}, journal = {Proc. SPIE}, doi = {10.1117/12.3022119}, pages = {PC130170X}, year = {2024}, language = {en} } @article{MuellerMartinezLahuertaSekulicetal.2025, author = {M{\"u}ller, Gabriel and Mart{\´i}nez-Lahuerta, Victor J. and Sekulic, Ivan and Burger, Sven and Schneider, Philipp-Immanuel and Gaaloul, Naceur}, title = {Bayesian optimization for state engineering of quantum gases}, volume = {10}, journal = {Quantum Sci. Technol.}, arxiv = {http://arxiv.org/abs/2404.18234}, doi = {10.1088/2058-9565/ad9050}, pages = {015033}, year = {2025}, language = {en} } @inproceedings{SchaibleWinartoSkorjancetal.2025, author = {Schaible, Jonas and Winarto, Hanifah and Skorjanc, Victor and Yoo, Danbi and Zimmermann, Lea and Wessels, Andreas and J{\"a}ger, Klaus and Sekulic, Ivan and Schneider, Philipp-Immanuel and Bl{\"a}si, Benedikt and Burger, Sven and Becker, Christiane}, title = {Optimization strategies for colorful thin film solar cells}, volume = {PC13361}, booktitle = {Proc. SPIE}, doi = {10.1117/12.3041408}, pages = {PC133610E}, year = {2025}, language = {en} } @inproceedings{KuenBetzBinkowskietal.2023, author = {Kuen, Lilli and Betz, Fridtjof and Binkowski, Felix and Schneider, Philipp-Immanuel and Hammerschmidt, Martin and Heermeier, Niels and Rodt, Sven and Reitzenstein, Stephan and Burger, Sven}, title = {Applying a Riesz-projection-based contour integral eigenvalue solver to compute resonance modes of a VCSEL}, volume = {12575}, booktitle = {Proc. SPIE}, arxiv = {http://arxiv.org/abs/2306.02926}, doi = {10.1117/12.2665490}, pages = {125750J}, year = {2023}, 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{SekulicSchneiderHammerschmidtetal.2025, author = {Sekulic, Ivan and Schneider, Philipp-Immanuel and Hammerschmidt, Martin and Schaible, Jonas and Burger, Sven}, title = {Physics informed Bayesian optimization for inverse design of diffractive optical elements}, volume = {PC13573}, booktitle = {Proc. SPIE}, doi = {10.1117/12.3064372}, pages = {PC135730R}, year = {2025}, 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} }