{"record_type":"pith_number_record","schema_url":"https://pith.science/schemas/pith-number/v1.json","pith_number":"pith:2020:QWDAJGPSS4A25IH2PEFPLNECFB","short_pith_number":"pith:QWDAJGPS","schema_version":"1.0","canonical_sha256":"85860499f29701aea0fa790af5b482285922c597ed8d97ce5fb9a861a43761f2","source":{"kind":"arxiv","id":"2012.04797","version":2},"attestation_state":"computed","paper":{"title":"Numerical Calabi-Yau metrics from holomorphic networks","license":"http://arxiv.org/licenses/nonexclusive-distrib/1.0/","headline":"","cross_cats":["math.CV","physics.comp-ph"],"primary_cat":"hep-th","authors_text":"Michael R. Douglas, Subramanian Lakshminarasimhan, Yidi Qi","submitted_at":"2020-12-09T00:15:56Z","abstract_excerpt":"We propose machine learning inspired methods for computing numerical Calabi-Yau (Ricci flat K\\\"ahler) metrics, and implement them using Tensorflow/Keras. We compare them with previous work, and find that they are far more accurate for manifolds with little or no symmetry. We also discuss issues such as overparameterization and choice of optimization methods."},"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":"2012.04797","kind":"arxiv","version":2},"metadata":{"license":"http://arxiv.org/licenses/nonexclusive-distrib/1.0/","primary_cat":"hep-th","submitted_at":"2020-12-09T00:15:56Z","cross_cats_sorted":["math.CV","physics.comp-ph"],"title_canon_sha256":"eabeea23d2d29526e1e5f00707e6d61ae3277ca056b15b9b4e30b07af2dc3bed","abstract_canon_sha256":"f0e90b34a52d7128ec289797bad93e28643173083c3f4230a8410e1838ac104d"},"schema_version":"1.0"},"receipt":{"kind":"pith_receipt","key_id":"pith-v1-2026-05","algorithm":"ed25519","signed_at":"2026-07-05T02:37:43.288356Z","signature_b64":"JIiyh91gWbWOQW9Dtur3EkjLqq2FjR++aIKB06kxAOXTHIHX6FjKeTXSgEVHGtaofSOGwm1iyQ3S0kwj5ee3AA==","signed_message":"canonical_sha256_bytes","builder_version":"pith-number-builder-2026-05-17-v1","receipt_version":"0.3","canonical_sha256":"85860499f29701aea0fa790af5b482285922c597ed8d97ce5fb9a861a43761f2","last_reissued_at":"2026-07-05T02:37:43.287814Z","signature_status":"signed_v1","first_computed_at":"2026-07-05T02:37:43.287814Z","public_key_fingerprint":"8d4b5ee74e4693bcd1df2446408b0d54"},"graph_snapshot":{"paper":{"title":"Numerical Calabi-Yau metrics from holomorphic networks","license":"http://arxiv.org/licenses/nonexclusive-distrib/1.0/","headline":"","cross_cats":["math.CV","physics.comp-ph"],"primary_cat":"hep-th","authors_text":"Michael R. Douglas, Subramanian Lakshminarasimhan, Yidi Qi","submitted_at":"2020-12-09T00:15:56Z","abstract_excerpt":"We propose machine learning inspired methods for computing numerical Calabi-Yau (Ricci flat K\\\"ahler) metrics, and implement them using Tensorflow/Keras. We compare them with previous work, and find that they are far more accurate for manifolds with little or no symmetry. We also discuss issues such as overparameterization and choice of optimization methods."},"claims":{"count":0,"items":[],"snapshot_sha256":"258153158e38e3291e3d48162225fcdb2d5a3ed65a07baac614ab91432fd4f57"},"source":{"id":"2012.04797","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/2012.04797/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":"2012.04797","created_at":"2026-07-05T02:37:43.287892+00:00"},{"alias_kind":"arxiv_version","alias_value":"2012.04797v2","created_at":"2026-07-05T02:37:43.287892+00:00"},{"alias_kind":"doi","alias_value":"10.48550/arxiv.2012.04797","created_at":"2026-07-05T02:37:43.287892+00:00"},{"alias_kind":"pith_short_12","alias_value":"QWDAJGPSS4A2","created_at":"2026-07-05T02:37:43.287892+00:00"},{"alias_kind":"pith_short_16","alias_value":"QWDAJGPSS4A25IH2","created_at":"2026-07-05T02:37:43.287892+00:00"},{"alias_kind":"pith_short_8","alias_value":"QWDAJGPS","created_at":"2026-07-05T02:37:43.287892+00:00"}],"events":[],"event_summary":{},"paper_claims":[],"inbound_citations":{"count":3,"internal_anchor_count":0,"sample":[{"citing_arxiv_id":"2606.25016","citing_title":"Lost in Translation: Moduli Stabilization from EFT to Eleven Dimensions","ref_index":22,"is_internal_anchor":false},{"citing_arxiv_id":"2604.25020","citing_title":"PINNs in More General Geometry","ref_index":9,"is_internal_anchor":false},{"citing_arxiv_id":"2604.04321","citing_title":"Minimising Willmore Energy via Neural Flow","ref_index":5,"is_internal_anchor":false}]},"formal_canon":{"evidence_count":0,"sample":[],"anchors":[]},"links":{"html":"https://pith.science/pith/QWDAJGPSS4A25IH2PEFPLNECFB","json":"https://pith.science/pith/QWDAJGPSS4A25IH2PEFPLNECFB.json","graph_json":"https://pith.science/api/pith-number/QWDAJGPSS4A25IH2PEFPLNECFB/graph.json","events_json":"https://pith.science/api/pith-number/QWDAJGPSS4A25IH2PEFPLNECFB/events.json","paper":"https://pith.science/paper/QWDAJGPS"},"agent_actions":{"view_html":"https://pith.science/pith/QWDAJGPSS4A25IH2PEFPLNECFB","download_json":"https://pith.science/pith/QWDAJGPSS4A25IH2PEFPLNECFB.json","view_paper":"https://pith.science/paper/QWDAJGPS","resolve_alias":"https://pith.science/api/pith-number/resolve?arxiv=2012.04797&json=true","fetch_graph":"https://pith.science/api/pith-number/QWDAJGPSS4A25IH2PEFPLNECFB/graph.json","fetch_events":"https://pith.science/api/pith-number/QWDAJGPSS4A25IH2PEFPLNECFB/events.json","actions":{"anchor_timestamp":"https://pith.science/pith/QWDAJGPSS4A25IH2PEFPLNECFB/action/timestamp_anchor","attest_storage":"https://pith.science/pith/QWDAJGPSS4A25IH2PEFPLNECFB/action/storage_attestation","attest_author":"https://pith.science/pith/QWDAJGPSS4A25IH2PEFPLNECFB/action/author_attestation","sign_citation":"https://pith.science/pith/QWDAJGPSS4A25IH2PEFPLNECFB/action/citation_signature","submit_replication":"https://pith.science/pith/QWDAJGPSS4A25IH2PEFPLNECFB/action/replication_record"}},"created_at":"2026-07-05T02:37:43.287892+00:00","updated_at":"2026-07-05T02:37:43.287892+00:00"}