[14] K. Eggensperger, P. Müller, N. Mallik, M. Feurer, R. Sass, A. Klein, N. Awad, M. Lindauer, and F. Hutter. HPOBench: A collection of reproducible multi-fidelity benchmark problems for HPO. In Advances in Neural Information Processing Systems (NeurIPS), Virtual, 2021. Datasets and Benchmarks Track.
[15] A. I. J. Forrester, A. Sobester, and A. J. Keane. Multi-fidelity optimization via surrogate modelling. Proceedings of the Royal Society A: Mathematical, Physical and Engineering Sciences, 463(2088):3251-3269, 2007.
[16] R. Garnett. Bayesian Optimization. Cambridge University Press, 2023.
[17] J. Grandidier, D. M. Callahan, J. N. Munday, and H. A. Atwater. Light absorption enhancement in thin-film solar cells using whispering gallery modes in dielectric nanospheres. Advanced Materials, 23(10):1272-1276, 2011.
[18] D. J. Griffiths. Introduction to electrodynamics. American Association of Physics Teachers, 2005.
[19] S. Haghanifar and P. W. Leu. Detailed balance analysis of vertical GaAs nanowire array solar cells: exceeding the Shockley Queisser limit. Optics Express, 30(10):16145-16158, 2022.
[20] S. Haganifar, M. McCourt, B. Cheng, J. Wuenschell, P. Ohodnicki, and P. W. Leu. Creating glasswing butterfly-inspired durable antifogging superomniphobic supertransmissive, superclear nanostructured glass through Bayesian learning and optimization. *Materials Horizons*, 6(8): 1632–1642, 2019.
[21] S. Haghanifar, M. McCourt, B. Cheng, J. Wuenschell, P. Ohodnicki, and P. W. Leu. Discovering high-performance broadband and broad angle antireflection surfaces by machine learning. Optica, 7(7):784-789, 2020.
[22] A. M. Hammond, A. Oskooi, S. G. Johnson, and S. E. Ralph. Photonic topology optimization with semiconductor-foundry design-rule constraints. Optics Express, 29(15):23916-23938, 2021.
[23] A. M. Hammond, A. Oskooi, M. Chen, Z. Lin, S. G. Johnson, and S. E. Ralph. High-performance hybrid time/frequency-domain topology optimization for large-scale photonics inverse design. Optics Express, 30(3):4467-4491, 2022.
[24] N. Hansen, A. Auger, R. Ros, S. Finck, and P. Pošík. Comparing results of 31 algorithms from the black-box optimization benchmarking BBOB-2009. In Proceedings of the Annual Conference on Genetic and Evolutionary Computation (GECCO), pages 1689–1696, Portland, Oregon, USA, 2010.
[25] C. R. Harris, K. J. Millman, S. J. van der Walt, R. Gommers, P. Virtanen, D. Cournaapeau, E. Wieser, J. Taylor, S. Berg, N. J. Smith, R. Kern, M. Picus, S. Hoyer, M. H. van Kerkwijk, M. Brett, A. Haldane, J. F. del Río, M. Wiebe, P. Peterson, P. Gérard-Marchant, K. Sheppard, T. Reddy, W. Weckesser, H. Abbasi, C. Gohlke, and T. E. Oliphant. Array programming with NumPy. Nature, 585:357-362, 2020.
[26] D. Hernández-Lobato, J. M. Hernández-Lobato, A. Shah, and R. P. Adams. Predictive entropy search for multi-objective Bayesian optimization. In Proceedings of the International Conference on Machine Learning (ICML), pages 1492–1501, New York, New York, USA, 2016.
[27] International Commission on Illumination (CIE). CIE standard illuminant D65. https://doi.org/10.25039/CIE.DS.hjfjmt59, 2022.
[28] S. Ioffe and C. Szegedy. Batch normalization: Accelerating deep network training by reducing internal covariate shift. In Proceedings of the International Conference on Machine Learning (ICML), pages 448-456, Lille, France, 2015.
[29] J.-M. Jin. The finite element method in electromagnetics. John Wiley & Sons, 2015.
[30] D. R. Jones, C. D. Perttunen, and B. E. Stuckman. Lipschitzian optimization without the Lipschitz constant. Journal of Optimization Theory and Applications, 79(1):157-181, 1993.