{"status":"ok","message-type":"work","message-version":"1.0.0","message":{"indexed":{"date-parts":[[2026,9,25]],"date-time":"2026-09-25T07:58:37Z","timestamp":1790323117912,"version":"4.1.0"},"reference-count":33,"publisher":"Wiley","issue":"18","license":[{"start":{"date-parts":[[2026,9,23]],"date-time":"2026-09-23T00:00:00Z","timestamp":1790121600000},"content-version":"vor","delay-in-days":0,"URL":"http:\/\/creativecommons.org\/licenses\/by\/4.0\/"},{"start":{"date-parts":[[2026,9,23]],"date-time":"2026-09-23T00:00:00Z","timestamp":1790121600000},"content-version":"tdm","delay-in-days":0,"URL":"http:\/\/doi.wiley.com\/10.1002\/tdm_license_1.1"}],"funder":[{"DOI":"10.13039\/100019489","name":"Optica Foundation","doi-asserted-by":"publisher","id":[{"id":"10.13039\/100019489","id-type":"DOI","asserted-by":"publisher"}]}],"content-domain":{"domain":["onlinelibrary.wiley.com"],"crossmark-restriction":true},"short-container-title":["Nanophotonics"],"published-print":{"date-parts":[[2026,9,25]]},"abstract":"<jats:title>ABSTRACT<\/jats:title>\n                  <jats:p>Optical computing offers a route to address the growing computational demands of machine learning, with inverse\u2010designed nanophotonic media providing a compact path to passive optical operators. Training such devices end to end remains expensive because each geometry update requires full\u2010wave forward and adjoint evaluations. We introduce a surrogate\u2010guided inverse\u2010design framework that separates task learning from electromagnetic realization. Classification is first solved in matrix space using a passive complex operator with bounded singular values, and the resulting target is transferred to a fabrication\u2010aware freeform device through complex transmission matching and reflection suppression. Because the realization target is fixed, the electromagnetic work per update is determined by the input and output port counts and is independent of training\u2010set size. A locality\u2010constrained router composed with a fixed evanescent mixing stage extends the approach to 16\u2010port transformations. Under the same physical initialization, solver, fabrication map, and 550\u2010solve budget, operator matching reached 98.34% test accuracy on MedNIST, compared with 89.50% for direct geometry\u2010to\u2010task training. On RSSCN7, the combined optical surrogate reached 48.21%, compared with 38.57% for a PCA\u201016 ridge classifier. Three Yin\u2013Yang realizations reached %. These results establish surrogate\u2010guided operator matching for compact, fabrication\u2010aware photonic neural networks.<\/jats:p>","DOI":"10.1002\/nap2.70302","type":"journal-article","created":{"date-parts":[[2026,9,23]],"date-time":"2026-09-23T12:24:00Z","timestamp":1790166240000},"update-policy":"https:\/\/doi.org\/10.1002\/crossmark_policy","source":"Crossref","is-referenced-by-count":0,"title":["Surrogate Scattering Matrix\u2010Guided Inverse Design of Nanophotonic Neural Networks"],"prefix":"10.1002","volume":"15","author":[{"given":"Azka Maula","family":"Iskandar Muda","sequence":"first","affiliation":[{"name":"Department of Electrical and Electronics Engineering Ko\u00e7 University  \u0130stanbul T\u00fcrkiye"}],"role":[{"vocabulary":"crossref","role":"author"}]},{"ORCID":"https:\/\/orcid.org\/0000-0002-4690-588X","authenticated-orcid":false,"given":"U\u011fur","family":"Te\u011fin","sequence":"additional","affiliation":[{"name":"Department of Electrical and Electronics Engineering Ko\u00e7 University  \u0130stanbul T\u00fcrkiye"}],"role":[{"vocabulary":"crossref","role":"author"}]}],"member":"311","published-online":{"date-parts":[[2026,9,23]]},"reference":[{"key":"e_1_2_13_2_1","doi-asserted-by":"publisher","DOI":"10.1038\/nphoton.2017.93"},{"key":"e_1_2_13_3_1","doi-asserted-by":"publisher","DOI":"10.1364\/OPTICA.5.000864"},{"key":"e_1_2_13_4_1","doi-asserted-by":"publisher","DOI":"10.1126\/science.ade8450"},{"key":"e_1_2_13_5_1","doi-asserted-by":"publisher","DOI":"10.1126\/science.aat8084"},{"key":"e_1_2_13_6_1","doi-asserted-by":"publisher","DOI":"10.1038\/s41586\u2010020\u201003070\u20101"},{"key":"e_1_2_13_7_1","doi-asserted-by":"publisher","DOI":"10.1038\/s41586\u2010022\u201004714\u20100"},{"key":"e_1_2_13_8_1","doi-asserted-by":"publisher","DOI":"10.1103\/PhysRevLett.73.58"},{"key":"e_1_2_13_9_1","doi-asserted-by":"publisher","DOI":"10.1364\/OPTICA.3.001460"},{"key":"e_1_2_13_10_1","doi-asserted-by":"publisher","DOI":"10.1038\/s41586\u2010020\u20102973\u20106"},{"key":"e_1_2_13_11_1","doi-asserted-by":"publisher","DOI":"10.1038\/s41377\u2010024\u201001590\u20103"},{"key":"e_1_2_13_12_1","doi-asserted-by":"publisher","DOI":"10.1038\/s41586\u2010025\u201009384\u20102"},{"key":"e_1_2_13_13_1","doi-asserted-by":"publisher","DOI":"10.1038\/s41586\u2010024\u201007687\u20104"},{"key":"e_1_2_13_14_1","doi-asserted-by":"publisher","DOI":"10.1126\/science.1242818"},{"key":"e_1_2_13_15_1","doi-asserted-by":"publisher","DOI":"10.1126\/science.aaw2498"},{"key":"e_1_2_13_16_1","doi-asserted-by":"publisher","DOI":"10.1038\/s41467\u2010021\u201021664\u20109"},{"key":"e_1_2_13_17_1","doi-asserted-by":"publisher","DOI":"10.1021\/acsphotonics.2c00373"},{"key":"e_1_2_13_18_1","doi-asserted-by":"publisher","DOI":"10.1038\/s41566\u2010018\u20100246\u20109"},{"key":"e_1_2_13_19_1","doi-asserted-by":"publisher","DOI":"10.1038\/s41598\u2010017\u201001939\u20102"},{"key":"e_1_2_13_20_1","doi-asserted-by":"publisher","DOI":"10.1364\/OE.21.021693"},{"key":"e_1_2_13_21_1","doi-asserted-by":"publisher","DOI":"10.1364\/PRJ.7.000823"},{"key":"e_1_2_13_22_1","doi-asserted-by":"publisher","DOI":"10.1038\/s41566\u2010024\u201001394\u20102"},{"key":"e_1_2_13_23_1","doi-asserted-by":"publisher","DOI":"10.1038\/s41467\u2010025\u201065213\u20100"},{"key":"e_1_2_13_24_1","doi-asserted-by":"publisher","DOI":"10.1038\/s41467\u2010026\u201068648\u20101"},{"key":"e_1_2_13_25_1","doi-asserted-by":"publisher","DOI":"10.1364\/OE.563512"},{"key":"e_1_2_13_26_1","doi-asserted-by":"publisher","DOI":"10.1017\/CBO9781139020411"},{"key":"e_1_2_13_27_1","first-page":"1517","volume-title":"Proceedings of the 36th International Conference on Machine Learning (ICML)","author":"Dao T.","year":"2019"},{"key":"e_1_2_13_28_1","unstructured":"Project MONAI.MedNIST Dataset MONAI Extra Test Data Release 0.8.1. [dataset] 2021 https:\/\/github.com\/Project\u2010MONAI\/MONAI\u2010extra\u2010test\u2010data\/releases\/download\/0.8.1\/MedNIST.tar.gz."},{"key":"e_1_2_13_29_1","doi-asserted-by":"publisher","DOI":"10.1109\/LGRS.2015.2475299"},{"key":"e_1_2_13_30_1","first-page":"107","volume-title":"Proceedings of the 2022 Annual Neuro\u2010Inspired Computational Elements Conference","author":"Kriener L.","year":"2022"},{"key":"e_1_2_13_31_1","doi-asserted-by":"publisher","DOI":"10.1002\/sapm19624111"},{"key":"e_1_2_13_32_1","volume-title":"Principal Component Analysis","author":"Jolliffe I. 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