[31] K. Kandasamy, G. Dasarathy, J. Schneider, and B. Poczos. Multi-fidelity Bayesian optimisation with continuous approximations. In Proceedings of the International Conference on Machine Learning (ICML), pages 1799-1808, Sydney, Australia, 2017.
[32] M. D. Kelzenberg, D. B. Turner-Evans, B. M. Kayes, M. A. Filler, M. C. Putnam, N. S. Lewis, and H. A. Atwater. Photovoltaic measurements in single-nanowire silicon solar cells. Nano Letters, 8(2):710-714, 2008.
[33] J. Kim and S. Choi. BayesO: A Bayesian optimization framework in Python. Journal of Open Source Software, 8(90):5320, 2023.
[34] S. Kim, P. Y. Lu, C. Loh, J. Smith, J. Snoek, and M. Soljacic. Deep learning for Bayesian optimization of scientific problems with high-dimensional structure. Transactions on Machine Learning Research, 2022.
[35] D. P. Kingma and J. L. Ba. Adam: A method for stochastic optimization. In Proceedings of the International Conference on Learning Representations (ICLR), San Diego, California, USA, 2015.
[36] A. Klein and F. Hutter. Tabular benchmarks for joint architecture and hyperparameter optimization. arXiv preprint arXiv:1905.04970, 2019.
[37] J. Knowles. ParEGO: a hybrid algorithm with on-line landscape approximation for expensive multiobjective optimization problems. IEEE Transactions on Evolutionary Computation, 10(1): 50-66, 2006.
[38] T. A. F. König, P. A. Ledin, J. Kerszulis, M. A. Mahmoud, M. A. El-Sayed, J. R. Reynolds, and V. V. Tsukruk. Electrically tunable plasmonic behavior of nanocube-polymer nanomaterials induced by a redox-active electrochromic polymer. ACS Nano, 8(6):6182-6192, 2014.
[39] M. Li, M. J. McCourt, A. J. Galante, and P. W. Leu. Bayesian optimization of nanophotonic electromagnetic shielding with very high visible transparency. Optics Express, 30(18):33182-33194, 2022.
[40] M. Li, M. Zarei, K. Mohammadi, S. B. Walker, M. LeMieux, and P. W. Leu. Silver meshes for record-performance transparent electromagnetic interference shielding. ACS Applied Materials & Interfaces, 15(25):30591-30599, 2023.
[41] I. H. Malitson. Interspecimen comparison of the refractive index of fused silica. Journal of the Optical Society of America, 55(10):1205-1209, 1965.
[42] R. A. Maniyara, V. K. Mkhitaryan, T. L. Chen, D. S. Ghosh, and V. Pruneri. An antireflection transparent conductor with ultralow optical loss ( $< 2\%$ ) and electrical resistance ( $< 6\omega \mathrm{sq}^{-1}$ ). Nature Communications, 7(1):13771, 2016.
[43] M. G. Moharam and T. K. Gaylord. Rigorous coupled-wave analysis of planar-grating diffraction. Journal of the Optical Society of America, 71(7):811-818, 1981.
[44] A. F. Oskooi, D. Roundy, M. Ibanescu, P. Bermel, J. D. Joannopoulos, and S. G. Johnson. MEEP: A flexible free-software package for electromagnetic simulations by the FDTD method. Computer Physics Communications, 181(3):687-702, 2010.
[45] A. Paszke, S. Gross, F. Massa, A. Lerer, J. Bradbury, G. Chanan, T. Killeen, Z. Lin, N. Gimelshein, L. Antiga, A. Desmaison, A. Köpf, E. Yang, Z. DeVito, M. Raison, A. Tejani, S. Chilamkurthy, B. Steiner, L. Fang, J. Bai, and S. Chintala. PyTorch: An imperative style, high-performance deep learning library. In Advances in Neural Information Processing Systems (NeurIPS), volume 32, pages 8026–8037, Vancouver, British Columbia, Canada, 2019.
[46] F. Pfisterer, L. Schneider, J. Moosbauer, M. Binder, and B. Bischl. YAPHO Gym – an efficient multi-objective multi-fidelity benchmark for hyperparameter optimization. In Proceedings of the International Conference on Automated Machine Learning (AutoML-Conf), Baltimore, Maryland, USA, 2022.