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However, the current demonstrated applications of DONNs are largely image classification tasks, which undermine the prospect of developing and utilizing such hardware for other ML applications. Herein, the deployment of an all\u2010optical reconfigurable DONNs system for scientific computing is demonstrated numerically and experimentally, including guiding two\u2010dimensional quantum material synthesis, predicting the properties of two\u2010dimensional quantum materials and small molecular cancer drugs, predicting the device response of nanopatterned integrated photonic power splitters, and the dynamic stabilization of an inverted pendulum with reinforcement learning. Despite a large variety of input data structures, a universal feature engineering approach is developed to convert categorical input features to images that can be processed in the DONNs system. The results open up new opportunities for employing DONNs systems for a broad range of ML applications.<\/jats:p>","DOI":"10.1002\/aisy.202300536","type":"journal-article","created":{"date-parts":[[2023,10,11]],"date-time":"2023-10-11T19:26:49Z","timestamp":1697052409000},"update-policy":"https:\/\/doi.org\/10.1002\/crossmark_policy","source":"Crossref","is-referenced-by-count":10,"title":["Scientific Computing with Diffractive Optical Neural Networks"],"prefix":"10.1002","volume":"5","author":[{"given":"Ruiyang","family":"Chen","sequence":"first","affiliation":[{"name":"Department of Electrical and Computer Engineering University of Utah  Salt Lake City UT 84112 USA"}]},{"given":"Yingheng","family":"Tang","sequence":"additional","affiliation":[{"name":"Department of Electrical and Computer Engineering University of Utah  Salt Lake City UT 84112 USA"},{"name":"School of Electrical and Computer Engineering and Birck Nanotechnology Center Purdue University  West Lafayette IN 47907 USA"}]},{"given":"Jianzhu","family":"Ma","sequence":"additional","affiliation":[{"name":"Institute for AI Industry Research Tsinghua University  Beijing 10084 China"}]},{"ORCID":"https:\/\/orcid.org\/0000-0003-3139-034X","authenticated-orcid":false,"given":"Weilu","family":"Gao","sequence":"additional","affiliation":[{"name":"Department of Electrical and Computer Engineering University of Utah  Salt Lake City UT 84112 USA"}]}],"member":"311","published-online":{"date-parts":[[2023,10,8]]},"reference":[{"key":"e_1_2_9_2_1","doi-asserted-by":"publisher","DOI":"10.1038\/nature14539"},{"key":"e_1_2_9_3_1","volume-title":"Deep Learning","author":"Goodfellow I.","year":"2016"},{"key":"e_1_2_9_4_1","doi-asserted-by":"publisher","DOI":"10.1038\/s41591-018-0300-7"},{"key":"e_1_2_9_5_1","doi-asserted-by":"publisher","DOI":"10.1007\/978-3-030-41068-1"},{"key":"e_1_2_9_6_1","doi-asserted-by":"publisher","DOI":"10.1038\/s41586-021-03544-w"},{"key":"e_1_2_9_7_1","doi-asserted-by":"publisher","DOI":"10.1038\/s41586-018-0337-2"},{"key":"e_1_2_9_8_1","doi-asserted-by":"publisher","DOI":"10.1038\/s41586-019-1923-7"},{"key":"e_1_2_9_9_1","doi-asserted-by":"publisher","DOI":"10.1109\/MCSE.2017.29"},{"key":"e_1_2_9_10_1","doi-asserted-by":"publisher","DOI":"10.1126\/science.aam9744"},{"key":"e_1_2_9_11_1","doi-asserted-by":"publisher","DOI":"10.1038\/nphoton.2017.93"},{"key":"e_1_2_9_12_1","doi-asserted-by":"publisher","DOI":"10.1038\/s41586-020-03070-1"},{"key":"e_1_2_9_13_1","doi-asserted-by":"publisher","DOI":"10.1038\/s41467-021-27774-8"},{"key":"e_1_2_9_14_1","doi-asserted-by":"publisher","DOI":"10.1364\/OE.20.003241"},{"key":"e_1_2_9_15_1","doi-asserted-by":"publisher","DOI":"10.1038\/ncomms4541"},{"key":"e_1_2_9_16_1","first-page":"041037","volume":"10","author":"Rafayelyan M.","year":"2020","journal-title":"Phys. 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