J. Eur. Opt. Society-Rapid Publ. 22, 27( 2026) 283
2 Chen H, Feng J, Jiang M, Wang Y, Lin J, Tan J, Jin P, Diffractive deep neural networks at visible wavelengths, Engineering 7, 1483 – 1491( 2021). https:// doi. org / 10.1016 / j. eng. 2020.07.032.
3 Fujita T, Sakaguchi H, Zhang J, Nonaka H, Sumi S, Awano H, Ishibashi T, Magneto-optical diffractive deep neural network, Opt. Express 30, 36889 – 36899( 2022). https:// doi. org / 10.1364 / OE. 470513.
4 Sakaguchi H, Fujita T, Zhang J, Sumi S, Awano H, Nonaka H, Ishibashi T, Development of fabrication techniques for magneto-optical diffractive deep neural networks, IEEE Trans. Magn. 59, 1 – 4( 2023). https:// doi. org / 10.1109 / TMAG. 2023.32818.4210.
5 Rumelhart DE, Hinton GE, Williams RJ, Learning representations by back-propagating errors, Nature 323, 533 – 536( 1986). https:// doi. org / 10.1038 / 323533a0.
6 Silver D, Huang A, Maddison CJ, Guez A, Sifre L, Driessche GD, Schrittwieser J, Antonoglou I, Panneershelvam V, Lanctot M, Dieleman S, Grewe D, Nham J, Kalchbrenner N, Sutskever I, Lillicrap T, Leach M, Kavukcuoglu K, Graepel T, Hassabis D, Mastering the game of Go with deep neural networks and tree search, Nature 529, 484 – 489( 2016). https:// doi. org / 10.1038 / nature16961.
7 Course K, Nair PB, State estimation of a physical system with unknown governing equations, Nature 622, 261 – 267( 2023). https:// doi. org / 10.1038 / s41586-023-06574-8. 8 Hao K, Bilionis I, Neural information field filter, Machine
Learning( 2024). https:// doi. org / 10.48550 / arXiv. 2407.16502.
9 Mukhopadhaya J, Whitehead BT, Quindlen JF, Alonso JJ, Multi-fidelity modeling of probabilistic aerodynamic databases for use in aerospace engineering, arXiv( 2019). https:// doi. org / 10.48550 / arXiv. 1911.05036.
10 Lee S, Kitahara M, Yaoyama T, Itoi T, Latent space-based Bayesian approach to the NASA and DNV challenge 2025, Proc. Conf.( 2025). https:// doi. org / 10.3850 / 978-981-94- 3281-3 _ ESREL-SRA-E2025-P8724-cd.
11 Butler RW, Caldwell JL, Carreno VA, Holloway CM, Miner PS, DiVito BL, NASA Langley’ s research and technology transfer program in formal methods, Proceedings of the Tenth Annual Conference on Computer Assurance( COM- PASS’ 95), IEEE, 101 – 110( 1995). https:// doi. org / 10.1109 / CMPASS. 1995.521893.
12 Sakaguchi H, Oya R, Sumi S, Awano H, Nonaka H, Chafi FZ, Ishibashi T, Development of online learning technique for magneto-optical diffractive deep neural networks, J. Phys. Conf. Ser. 3161-012042( 2026). https:// doi. org / 10.1088 / 1742-6596 / 3161 / 1 / 012042.
13 Matsushima K, Shimobaba T, Band-limited angular spectrum method for numerical simulation of free-space propagation in far and near fields, Opt. Express 17, 19662 – 19676( 2009). https:// doi. org / 10.1364 / OE. 17.019662.
14 Goodman JW, Introduction to Fourier Optics, 4th ed. W. H. Freeman, New York( 2017).).
15 Nagakubo Y, Baba Y, Liu Q, Lou G, Ishibashi T, Development of MO imaging plate for MO color imaging, J. Magn. Soc. Jpn., 41, 29 – 33( 2017). https:// doi. org / 10.3379 / msjmag. 1701R003.
16 Sakaguchi H, Watanabe K, Ikeda J, Sumi S, Awano H, Chafi FZ, Ishibashi T, Reconfigurable magneto-optical diffractive neural network with enhanced optical phase modulation, Sc. Rep.( 2026). https:// doi. org / 10.1038 / s41598-026-42193-9.
17 Sasaki M, Lou G, Liu Q, Ninomiya M, Kato T, Iwata S, Ishibashi T, Nd 0. 5 Bi 2. 5 Fe 5-y Ga y O 12 thin films on Gd 3 Ga 5 O 12 substrates prepared by metal-organic decomposition, Jpn. J. Appl. 55,( 2016). https:// doi. org / 10.7567 / JJAP. 55. 055501.
18 Jesenska E, Yoshida T, Shinozaki K, Ishibashi T, Beran L, Zahradnik M, Antos R, Kučera M, Veis M, Optical and magneto-optical properties of Bi substituted yttrium iron garnets prepared by metal organic decomposition, Opt. Mater. Express 6 1986 – 1997( 2016). https:// doi. org / 10.1364 / OME. 6.001986.