{"record_type":"pith_number_record","schema_url":"https://pith.science/schemas/pith-number/v1.json","pith_number":"pith:2017:OYCGYZQCOR7MQZWZ5X7IJCIRRC","short_pith_number":"pith:OYCGYZQC","schema_version":"1.0","canonical_sha256":"76046c6602747ec866d9edfe84891188bfa6304600a0ab7af3f6c95738a07fb2","source":{"kind":"arxiv","id":"1710.04724","version":3},"attestation_state":"computed","paper":{"title":"Training deep neural networks for the inverse design of nanophotonic structures","license":"http://arxiv.org/licenses/nonexclusive-distrib/1.0/","headline":"","cross_cats":["physics.app-ph"],"primary_cat":"physics.optics","authors_text":"Dianjing Liu, Erfan Khoram, Yixuan Tan, Zongfu Yu","submitted_at":"2017-10-12T21:27:34Z","abstract_excerpt":"Data inconsistency leads to a slow training process when deep neural networks are used for the inverse design of photonic devices, an issue that arises from the fundamental property of non-uniqueness in all inverse scattering problems. Here we show that by combining forward modeling and inverse design in a tandem architecture, one can overcome this fundamental issue, allowing deep neural networks to be effectively trained by data sets that contain non-unique electromagnetic scattering instances. This paves the way for using deep neural networks to design complex photonic structures that requir"},"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":"1710.04724","kind":"arxiv","version":3},"metadata":{"license":"http://arxiv.org/licenses/nonexclusive-distrib/1.0/","primary_cat":"physics.optics","submitted_at":"2017-10-12T21:27:34Z","cross_cats_sorted":["physics.app-ph"],"title_canon_sha256":"36a38d8ffdef6b4a7d341b4a4a513c01b1023c8dfdbb8fa1cbdf5699b73db218","abstract_canon_sha256":"daa2193b0d637e5a5788358bab3cf5050d3d59cd9d911874a91289a3efd9073b"},"schema_version":"1.0"},"receipt":{"kind":"pith_receipt","key_id":"pith-v1-2026-05","algorithm":"ed25519","signed_at":"2026-05-18T00:19:08.685250Z","signature_b64":"wRVFfjda4Utc4dUyx511VkK81ablZM0QlKlswWuPur+zyBHop5K785Fpg0kaU4gtxfT3+5JRGqZV9/809MTJCw==","signed_message":"canonical_sha256_bytes","builder_version":"pith-number-builder-2026-05-17-v1","receipt_version":"0.3","canonical_sha256":"76046c6602747ec866d9edfe84891188bfa6304600a0ab7af3f6c95738a07fb2","last_reissued_at":"2026-05-18T00:19:08.684562Z","signature_status":"signed_v1","first_computed_at":"2026-05-18T00:19:08.684562Z","public_key_fingerprint":"8d4b5ee74e4693bcd1df2446408b0d54"},"graph_snapshot":{"paper":{"title":"Training deep neural networks for the inverse design of nanophotonic structures","license":"http://arxiv.org/licenses/nonexclusive-distrib/1.0/","headline":"","cross_cats":["physics.app-ph"],"primary_cat":"physics.optics","authors_text":"Dianjing Liu, Erfan Khoram, Yixuan Tan, Zongfu Yu","submitted_at":"2017-10-12T21:27:34Z","abstract_excerpt":"Data inconsistency leads to a slow training process when deep neural networks are used for the inverse design of photonic devices, an issue that arises from the fundamental property of non-uniqueness in all inverse scattering problems. Here we show that by combining forward modeling and inverse design in a tandem architecture, one can overcome this fundamental issue, allowing deep neural networks to be effectively trained by data sets that contain non-unique electromagnetic scattering instances. This paves the way for using deep neural networks to design complex photonic structures that requir"},"claims":{"count":0,"items":[],"snapshot_sha256":"258153158e38e3291e3d48162225fcdb2d5a3ed65a07baac614ab91432fd4f57"},"source":{"id":"1710.04724","kind":"arxiv","version":3},"verdict":{"id":null,"model_set":{},"created_at":null,"strongest_claim":"","one_line_summary":"","pipeline_version":null,"weakest_assumption":"","pith_extraction_headline":""},"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":"1710.04724","created_at":"2026-05-18T00:19:08.684668+00:00"},{"alias_kind":"arxiv_version","alias_value":"1710.04724v3","created_at":"2026-05-18T00:19:08.684668+00:00"},{"alias_kind":"doi","alias_value":"10.48550/arxiv.1710.04724","created_at":"2026-05-18T00:19:08.684668+00:00"},{"alias_kind":"pith_short_12","alias_value":"OYCGYZQCOR7M","created_at":"2026-05-18T12:31:34.259226+00:00"},{"alias_kind":"pith_short_16","alias_value":"OYCGYZQCOR7MQZWZ","created_at":"2026-05-18T12:31:34.259226+00:00"},{"alias_kind":"pith_short_8","alias_value":"OYCGYZQC","created_at":"2026-05-18T12:31:34.259226+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/OYCGYZQCOR7MQZWZ5X7IJCIRRC","json":"https://pith.science/pith/OYCGYZQCOR7MQZWZ5X7IJCIRRC.json","graph_json":"https://pith.science/api/pith-number/OYCGYZQCOR7MQZWZ5X7IJCIRRC/graph.json","events_json":"https://pith.science/api/pith-number/OYCGYZQCOR7MQZWZ5X7IJCIRRC/events.json","paper":"https://pith.science/paper/OYCGYZQC"},"agent_actions":{"view_html":"https://pith.science/pith/OYCGYZQCOR7MQZWZ5X7IJCIRRC","download_json":"https://pith.science/pith/OYCGYZQCOR7MQZWZ5X7IJCIRRC.json","view_paper":"https://pith.science/paper/OYCGYZQC","resolve_alias":"https://pith.science/api/pith-number/resolve?arxiv=1710.04724&json=true","fetch_graph":"https://pith.science/api/pith-number/OYCGYZQCOR7MQZWZ5X7IJCIRRC/graph.json","fetch_events":"https://pith.science/api/pith-number/OYCGYZQCOR7MQZWZ5X7IJCIRRC/events.json","actions":{"anchor_timestamp":"https://pith.science/pith/OYCGYZQCOR7MQZWZ5X7IJCIRRC/action/timestamp_anchor","attest_storage":"https://pith.science/pith/OYCGYZQCOR7MQZWZ5X7IJCIRRC/action/storage_attestation","attest_author":"https://pith.science/pith/OYCGYZQCOR7MQZWZ5X7IJCIRRC/action/author_attestation","sign_citation":"https://pith.science/pith/OYCGYZQCOR7MQZWZ5X7IJCIRRC/action/citation_signature","submit_replication":"https://pith.science/pith/OYCGYZQCOR7MQZWZ5X7IJCIRRC/action/replication_record"}},"created_at":"2026-05-18T00:19:08.684668+00:00","updated_at":"2026-05-18T00:19:08.684668+00:00"}