{"record_type":"pith_number_record","schema_url":"https://pith.science/schemas/pith-number/v1.json","pith_number":"pith:2024:2FYQYJ3UEOLUKRU5BT4QRRCXRM","short_pith_number":"pith:2FYQYJ3U","schema_version":"1.0","canonical_sha256":"d1710c2774239745469d0cf908c4578b230836665816cdd65895c9644637b5f8","source":{"kind":"arxiv","id":"2406.12904","version":1},"attestation_state":"computed","paper":{"title":"Meent: Differentiable Electromagnetic Simulator for Machine Learning","license":"http://creativecommons.org/licenses/by/4.0/","headline":"","cross_cats":["physics.comp-ph","physics.optics"],"primary_cat":"cs.LG","authors_text":"Anthony W. Jung, Chaejin Park, Chanhyung Park, Chan Y. Park, Doyoung Heo, Jeongmin Shin, Jinmyoung Lee, Juho Park, Kevin Octavian, Min Seok Jang, Sangjun Han, Sanmun Kim, Seolho Kim, Sunghyun Nam, Yongha Kim","submitted_at":"2024-06-11T10:00:06Z","abstract_excerpt":"Electromagnetic (EM) simulation plays a crucial role in analyzing and designing devices with sub-wavelength scale structures such as solar cells, semiconductor devices, image sensors, future displays and integrated photonic devices. Specifically, optics problems such as estimating semiconductor device structures and designing nanophotonic devices provide intriguing research topics with far-reaching real world impact. Traditional algorithms for such tasks require iteratively refining parameters through simulations, which often yield sub-optimal results due to the high computational cost of both"},"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":"2406.12904","kind":"arxiv","version":1},"metadata":{"license":"http://creativecommons.org/licenses/by/4.0/","primary_cat":"cs.LG","submitted_at":"2024-06-11T10:00:06Z","cross_cats_sorted":["physics.comp-ph","physics.optics"],"title_canon_sha256":"df259fd5df509e3caf3ea343bfec8a49331f847ca790e72e7db42374ef9d2a9b","abstract_canon_sha256":"7ca32ebc2984ccd8654785cf0f038f160ea3cddc09c6cd595510e081c9073a0e"},"schema_version":"1.0"},"receipt":{"kind":"pith_receipt","key_id":"pith-v1-2026-05","algorithm":"ed25519","signed_at":"2026-07-05T08:33:53.141842Z","signature_b64":"EVclk4hKdcv7r9eioxnyB5gPONbCSNtDfx4/aqzC+xQFbvLWGP/kcbfeZb3QkJprOpgYCVjB8XplDTZKeP5fDQ==","signed_message":"canonical_sha256_bytes","builder_version":"pith-number-builder-2026-05-17-v1","receipt_version":"0.3","canonical_sha256":"d1710c2774239745469d0cf908c4578b230836665816cdd65895c9644637b5f8","last_reissued_at":"2026-07-05T08:33:53.141382Z","signature_status":"signed_v1","first_computed_at":"2026-07-05T08:33:53.141382Z","public_key_fingerprint":"8d4b5ee74e4693bcd1df2446408b0d54"},"graph_snapshot":{"paper":{"title":"Meent: Differentiable Electromagnetic Simulator for Machine Learning","license":"http://creativecommons.org/licenses/by/4.0/","headline":"","cross_cats":["physics.comp-ph","physics.optics"],"primary_cat":"cs.LG","authors_text":"Anthony W. Jung, Chaejin Park, Chanhyung Park, Chan Y. Park, Doyoung Heo, Jeongmin Shin, Jinmyoung Lee, Juho Park, Kevin Octavian, Min Seok Jang, Sangjun Han, Sanmun Kim, Seolho Kim, Sunghyun Nam, Yongha Kim","submitted_at":"2024-06-11T10:00:06Z","abstract_excerpt":"Electromagnetic (EM) simulation plays a crucial role in analyzing and designing devices with sub-wavelength scale structures such as solar cells, semiconductor devices, image sensors, future displays and integrated photonic devices. Specifically, optics problems such as estimating semiconductor device structures and designing nanophotonic devices provide intriguing research topics with far-reaching real world impact. Traditional algorithms for such tasks require iteratively refining parameters through simulations, which often yield sub-optimal results due to the high computational cost of both"},"claims":{"count":0,"items":[],"snapshot_sha256":"258153158e38e3291e3d48162225fcdb2d5a3ed65a07baac614ab91432fd4f57"},"source":{"id":"2406.12904","kind":"arxiv","version":1},"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/2406.12904/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":"2406.12904","created_at":"2026-07-05T08:33:53.141440+00:00"},{"alias_kind":"arxiv_version","alias_value":"2406.12904v1","created_at":"2026-07-05T08:33:53.141440+00:00"},{"alias_kind":"doi","alias_value":"10.48550/arxiv.2406.12904","created_at":"2026-07-05T08:33:53.141440+00:00"},{"alias_kind":"pith_short_12","alias_value":"2FYQYJ3UEOLU","created_at":"2026-07-05T08:33:53.141440+00:00"},{"alias_kind":"pith_short_16","alias_value":"2FYQYJ3UEOLUKRU5","created_at":"2026-07-05T08:33:53.141440+00:00"},{"alias_kind":"pith_short_8","alias_value":"2FYQYJ3U","created_at":"2026-07-05T08:33:53.141440+00:00"}],"events":[],"event_summary":{},"paper_claims":[],"inbound_citations":{"count":2,"internal_anchor_count":1,"sample":[{"citing_arxiv_id":"2607.07682","citing_title":"Neural Operator-enabled Topology-informed Evolutionary Strategy for PDE-Constrained Optimization","ref_index":48,"is_internal_anchor":true},{"citing_arxiv_id":"2512.08614","citing_title":"PyMieDiff: A differentiable Mie scattering library","ref_index":19,"is_internal_anchor":false}]},"formal_canon":{"evidence_count":0,"sample":[],"anchors":[]},"links":{"html":"https://pith.science/pith/2FYQYJ3UEOLUKRU5BT4QRRCXRM","json":"https://pith.science/pith/2FYQYJ3UEOLUKRU5BT4QRRCXRM.json","graph_json":"https://pith.science/api/pith-number/2FYQYJ3UEOLUKRU5BT4QRRCXRM/graph.json","events_json":"https://pith.science/api/pith-number/2FYQYJ3UEOLUKRU5BT4QRRCXRM/events.json","paper":"https://pith.science/paper/2FYQYJ3U"},"agent_actions":{"view_html":"https://pith.science/pith/2FYQYJ3UEOLUKRU5BT4QRRCXRM","download_json":"https://pith.science/pith/2FYQYJ3UEOLUKRU5BT4QRRCXRM.json","view_paper":"https://pith.science/paper/2FYQYJ3U","resolve_alias":"https://pith.science/api/pith-number/resolve?arxiv=2406.12904&json=true","fetch_graph":"https://pith.science/api/pith-number/2FYQYJ3UEOLUKRU5BT4QRRCXRM/graph.json","fetch_events":"https://pith.science/api/pith-number/2FYQYJ3UEOLUKRU5BT4QRRCXRM/events.json","actions":{"anchor_timestamp":"https://pith.science/pith/2FYQYJ3UEOLUKRU5BT4QRRCXRM/action/timestamp_anchor","attest_storage":"https://pith.science/pith/2FYQYJ3UEOLUKRU5BT4QRRCXRM/action/storage_attestation","attest_author":"https://pith.science/pith/2FYQYJ3UEOLUKRU5BT4QRRCXRM/action/author_attestation","sign_citation":"https://pith.science/pith/2FYQYJ3UEOLUKRU5BT4QRRCXRM/action/citation_signature","submit_replication":"https://pith.science/pith/2FYQYJ3UEOLUKRU5BT4QRRCXRM/action/replication_record"}},"created_at":"2026-07-05T08:33:53.141440+00:00","updated_at":"2026-07-05T08:33:53.141440+00:00"}