{"record_type":"pith_number_record","schema_url":"https://pith.science/schemas/pith-number/v1.json","pith_number":"pith:2025:AG26XGWL5QT3LMEUIUTUP4SLNK","short_pith_number":"pith:AG26XGWL","schema_version":"1.0","canonical_sha256":"01b5eb9acbec27b5b094452747f24b6aa389236577567967acbd6a4bb78496e9","source":{"kind":"arxiv","id":"2506.20056","version":2},"attestation_state":"computed","paper":{"title":"Machine-Learning-Assisted Photonic Device Development: A Multiscale Approach from Theory to Characterization","license":"http://creativecommons.org/licenses/by/4.0/","headline":"","cross_cats":["cs.LG"],"primary_cat":"physics.optics","authors_text":"Alexander Montes McNeil, Alexander V. Kildishev, Alexandra Boltasseva, Blake A. Wilson, Daksh Kumar Singh, Geetika Chitturi, Jae-Ik Choi, Michael Bezick, Michael Moebius, Peigang Chen, Pravin Mahendran, Rohan Ojha, Taehyuk Park, Trang Do, Vaishnavi Iyer, Vladimir M. Shalaev, Wenshan Cai, Yongmin Liu, Yuheng Chen","submitted_at":"2025-06-24T23:32:54Z","abstract_excerpt":"Photonic device development (PDD) has achieved remarkable success in designing and implementing new devices for controlling light across various wavelengths, scales, and applications, including telecommunications, imaging, sensing, and quantum information processing. PDD is an iterative, five-step process that consists of: i) deriving device behavior from design parameters, ii) simulating device performance, iii) finding the optimal candidate designs from simulations, iv) fabricating the optimal device, and v) measuring device performance. Classically, all these steps involve Bayesian optimiza"},"verification_status":{"content_addressed":true,"pith_receipt":true,"author_attested":false,"weak_author_claims":0,"strong_author_claims":0,"externally_anchored":false,"storage_verified":false,"citation_signatures":0,"replication_records":0,"graph_snapshot":true,"references_resolved":false,"formal_links_present":false},"canonical_record":{"source":{"id":"2506.20056","kind":"arxiv","version":2},"metadata":{"license":"http://creativecommons.org/licenses/by/4.0/","primary_cat":"physics.optics","submitted_at":"2025-06-24T23:32:54Z","cross_cats_sorted":["cs.LG"],"title_canon_sha256":"d6657f02e9f2fef3889afbd5dcd20d957e9cc010f4637407edbc9bde244583ee","abstract_canon_sha256":"74ca4ae2d05a66ba871b73be83d5eb77c26f2038a092ca9caa1db640d514eb12"},"schema_version":"1.0"},"receipt":{"kind":"pith_receipt","key_id":"pith-v1-2026-05","algorithm":"ed25519","signed_at":"2026-07-05T11:43:33.080726Z","signature_b64":"qzi3eY+LdjmWHFiDEs1VNUosz4EkB60d/nDwpASQqmps8iysnp5QR62ny9b7+w0L2+JKP6jkCfCMIz1Su+5XCQ==","signed_message":"canonical_sha256_bytes","builder_version":"pith-number-builder-2026-05-17-v1","receipt_version":"0.3","canonical_sha256":"01b5eb9acbec27b5b094452747f24b6aa389236577567967acbd6a4bb78496e9","last_reissued_at":"2026-07-05T11:43:33.080242Z","signature_status":"signed_v1","first_computed_at":"2026-07-05T11:43:33.080242Z","public_key_fingerprint":"8d4b5ee74e4693bcd1df2446408b0d54"},"graph_snapshot":{"paper":{"title":"Machine-Learning-Assisted Photonic Device Development: A Multiscale Approach from Theory to Characterization","license":"http://creativecommons.org/licenses/by/4.0/","headline":"","cross_cats":["cs.LG"],"primary_cat":"physics.optics","authors_text":"Alexander Montes McNeil, Alexander V. Kildishev, Alexandra Boltasseva, Blake A. Wilson, Daksh Kumar Singh, Geetika Chitturi, Jae-Ik Choi, Michael Bezick, Michael Moebius, Peigang Chen, Pravin Mahendran, Rohan Ojha, Taehyuk Park, Trang Do, Vaishnavi Iyer, Vladimir M. Shalaev, Wenshan Cai, Yongmin Liu, Yuheng Chen","submitted_at":"2025-06-24T23:32:54Z","abstract_excerpt":"Photonic device development (PDD) has achieved remarkable success in designing and implementing new devices for controlling light across various wavelengths, scales, and applications, including telecommunications, imaging, sensing, and quantum information processing. PDD is an iterative, five-step process that consists of: i) deriving device behavior from design parameters, ii) simulating device performance, iii) finding the optimal candidate designs from simulations, iv) fabricating the optimal device, and v) measuring device performance. Classically, all these steps involve Bayesian optimiza"},"claims":{"count":0,"items":[],"snapshot_sha256":"258153158e38e3291e3d48162225fcdb2d5a3ed65a07baac614ab91432fd4f57"},"source":{"id":"2506.20056","kind":"arxiv","version":2},"verdict":{"id":null,"model_set":{},"created_at":null,"strongest_claim":"","one_line_summary":"","pipeline_version":null,"weakest_assumption":"","pith_extraction_headline":""},"integrity":{"clean":true,"summary":{"advisory":0,"critical":0,"by_detector":{},"informational":0},"endpoint":"/pith/2506.20056/integrity.json","findings":[],"available":true,"detectors_run":[],"snapshot_sha256":"c28c3603d3b5d939e8dc4c7e95fa8dfce3d595e45f758748cecf8e644a296938"},"references":{"count":0,"sample":[],"resolved_work":0,"snapshot_sha256":"258153158e38e3291e3d48162225fcdb2d5a3ed65a07baac614ab91432fd4f57","internal_anchors":0},"formal_canon":{"evidence_count":0,"snapshot_sha256":"258153158e38e3291e3d48162225fcdb2d5a3ed65a07baac614ab91432fd4f57"},"author_claims":{"count":0,"strong_count":0,"snapshot_sha256":"258153158e38e3291e3d48162225fcdb2d5a3ed65a07baac614ab91432fd4f57"},"builder_version":"pith-number-builder-2026-05-17-v1"},"aliases":[{"alias_kind":"arxiv","alias_value":"2506.20056","created_at":"2026-07-05T11:43:33.080297+00:00"},{"alias_kind":"arxiv_version","alias_value":"2506.20056v2","created_at":"2026-07-05T11:43:33.080297+00:00"},{"alias_kind":"doi","alias_value":"10.48550/arxiv.2506.20056","created_at":"2026-07-05T11:43:33.080297+00:00"},{"alias_kind":"pith_short_12","alias_value":"AG26XGWL5QT3","created_at":"2026-07-05T11:43:33.080297+00:00"},{"alias_kind":"pith_short_16","alias_value":"AG26XGWL5QT3LMEU","created_at":"2026-07-05T11:43:33.080297+00:00"},{"alias_kind":"pith_short_8","alias_value":"AG26XGWL","created_at":"2026-07-05T11:43:33.080297+00:00"}],"events":[],"event_summary":{},"paper_claims":[],"inbound_citations":{"count":0,"internal_anchor_count":0,"sample":[]},"formal_canon":{"evidence_count":0,"sample":[],"anchors":[]},"links":{"html":"https://pith.science/pith/AG26XGWL5QT3LMEUIUTUP4SLNK","json":"https://pith.science/pith/AG26XGWL5QT3LMEUIUTUP4SLNK.json","graph_json":"https://pith.science/api/pith-number/AG26XGWL5QT3LMEUIUTUP4SLNK/graph.json","events_json":"https://pith.science/api/pith-number/AG26XGWL5QT3LMEUIUTUP4SLNK/events.json","paper":"https://pith.science/paper/AG26XGWL"},"agent_actions":{"view_html":"https://pith.science/pith/AG26XGWL5QT3LMEUIUTUP4SLNK","download_json":"https://pith.science/pith/AG26XGWL5QT3LMEUIUTUP4SLNK.json","view_paper":"https://pith.science/paper/AG26XGWL","resolve_alias":"https://pith.science/api/pith-number/resolve?arxiv=2506.20056&json=true","fetch_graph":"https://pith.science/api/pith-number/AG26XGWL5QT3LMEUIUTUP4SLNK/graph.json","fetch_events":"https://pith.science/api/pith-number/AG26XGWL5QT3LMEUIUTUP4SLNK/events.json","actions":{"anchor_timestamp":"https://pith.science/pith/AG26XGWL5QT3LMEUIUTUP4SLNK/action/timestamp_anchor","attest_storage":"https://pith.science/pith/AG26XGWL5QT3LMEUIUTUP4SLNK/action/storage_attestation","attest_author":"https://pith.science/pith/AG26XGWL5QT3LMEUIUTUP4SLNK/action/author_attestation","sign_citation":"https://pith.science/pith/AG26XGWL5QT3LMEUIUTUP4SLNK/action/citation_signature","submit_replication":"https://pith.science/pith/AG26XGWL5QT3LMEUIUTUP4SLNK/action/replication_record"}},"created_at":"2026-07-05T11:43:33.080297+00:00","updated_at":"2026-07-05T11:43:33.080297+00:00"}