{"record_type":"pith_number_record","schema_url":"https://pith.science/schemas/pith-number/v1.json","pith_number":"pith:2025:MREEELXRDFYZXWCBLHCKJ554PN","short_pith_number":"pith:MREEELXR","schema_version":"1.0","canonical_sha256":"6448422ef119719bd84159c4a4f7bc7b48b515c7492a1d0a7d3d9ba7307d37ee","source":{"kind":"arxiv","id":"2503.04776","version":1},"attestation_state":"computed","paper":{"title":"GrainPaint: A multi-scale diffusion-based generative model for microstructure reconstruction of large-scale objects","license":"http://arxiv.org/licenses/nonexclusive-distrib/1.0/","headline":"","cross_cats":["cond-mat.mtrl-sci","cs.CV","cs.LG"],"primary_cat":"cs.GR","authors_text":"Anh Tran, Cashen Diniz, Dehao Liu, Mark Fuge, Nathan Hoffman, Theron Rodgers","submitted_at":"2025-02-18T14:13:32Z","abstract_excerpt":"Simulation-based approaches to microstructure generation can suffer from a variety of limitations, such as high memory usage, long computational times, and difficulties in generating complex geometries. Generative machine learning models present a way around these issues, but they have previously been limited by the fixed size of their generation area. We present a new microstructure generation methodology leveraging advances in inpainting using denoising diffusion models to overcome this generation area limitation. We show that microstructures generated with the presented methodology are stat"},"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":"2503.04776","kind":"arxiv","version":1},"metadata":{"license":"http://arxiv.org/licenses/nonexclusive-distrib/1.0/","primary_cat":"cs.GR","submitted_at":"2025-02-18T14:13:32Z","cross_cats_sorted":["cond-mat.mtrl-sci","cs.CV","cs.LG"],"title_canon_sha256":"30823a93c9edcb9394502867bb57c4bcd41d4c8decc56d7f30109a364bf98a61","abstract_canon_sha256":"04bcd2d45b1229ea5d2d202f501bb46825f8486d506ed33b59fd65ba651e977f"},"schema_version":"1.0"},"receipt":{"kind":"pith_receipt","key_id":"pith-v1-2026-05","algorithm":"ed25519","signed_at":"2026-07-05T10:25:48.698493Z","signature_b64":"kWBigvqZjjKKMwQfu2hOvwJHqftrTxc6eHNRiyOhKk2jAoZV55LxpQV7KYeDajXGjulq74HfBVS2aLIxVUGPBg==","signed_message":"canonical_sha256_bytes","builder_version":"pith-number-builder-2026-05-17-v1","receipt_version":"0.3","canonical_sha256":"6448422ef119719bd84159c4a4f7bc7b48b515c7492a1d0a7d3d9ba7307d37ee","last_reissued_at":"2026-07-05T10:25:48.697472Z","signature_status":"signed_v1","first_computed_at":"2026-07-05T10:25:48.697472Z","public_key_fingerprint":"8d4b5ee74e4693bcd1df2446408b0d54"},"graph_snapshot":{"paper":{"title":"GrainPaint: A multi-scale diffusion-based generative model for microstructure reconstruction of large-scale objects","license":"http://arxiv.org/licenses/nonexclusive-distrib/1.0/","headline":"","cross_cats":["cond-mat.mtrl-sci","cs.CV","cs.LG"],"primary_cat":"cs.GR","authors_text":"Anh Tran, Cashen Diniz, Dehao Liu, Mark Fuge, Nathan Hoffman, Theron Rodgers","submitted_at":"2025-02-18T14:13:32Z","abstract_excerpt":"Simulation-based approaches to microstructure generation can suffer from a variety of limitations, such as high memory usage, long computational times, and difficulties in generating complex geometries. Generative machine learning models present a way around these issues, but they have previously been limited by the fixed size of their generation area. We present a new microstructure generation methodology leveraging advances in inpainting using denoising diffusion models to overcome this generation area limitation. We show that microstructures generated with the presented methodology are stat"},"claims":{"count":0,"items":[],"snapshot_sha256":"258153158e38e3291e3d48162225fcdb2d5a3ed65a07baac614ab91432fd4f57"},"source":{"id":"2503.04776","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/2503.04776/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":"2503.04776","created_at":"2026-07-05T10:25:48.697623+00:00"},{"alias_kind":"arxiv_version","alias_value":"2503.04776v1","created_at":"2026-07-05T10:25:48.697623+00:00"},{"alias_kind":"doi","alias_value":"10.48550/arxiv.2503.04776","created_at":"2026-07-05T10:25:48.697623+00:00"},{"alias_kind":"pith_short_12","alias_value":"MREEELXRDFYZ","created_at":"2026-07-05T10:25:48.697623+00:00"},{"alias_kind":"pith_short_16","alias_value":"MREEELXRDFYZXWCB","created_at":"2026-07-05T10:25:48.697623+00:00"},{"alias_kind":"pith_short_8","alias_value":"MREEELXR","created_at":"2026-07-05T10:25:48.697623+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/MREEELXRDFYZXWCBLHCKJ554PN","json":"https://pith.science/pith/MREEELXRDFYZXWCBLHCKJ554PN.json","graph_json":"https://pith.science/api/pith-number/MREEELXRDFYZXWCBLHCKJ554PN/graph.json","events_json":"https://pith.science/api/pith-number/MREEELXRDFYZXWCBLHCKJ554PN/events.json","paper":"https://pith.science/paper/MREEELXR"},"agent_actions":{"view_html":"https://pith.science/pith/MREEELXRDFYZXWCBLHCKJ554PN","download_json":"https://pith.science/pith/MREEELXRDFYZXWCBLHCKJ554PN.json","view_paper":"https://pith.science/paper/MREEELXR","resolve_alias":"https://pith.science/api/pith-number/resolve?arxiv=2503.04776&json=true","fetch_graph":"https://pith.science/api/pith-number/MREEELXRDFYZXWCBLHCKJ554PN/graph.json","fetch_events":"https://pith.science/api/pith-number/MREEELXRDFYZXWCBLHCKJ554PN/events.json","actions":{"anchor_timestamp":"https://pith.science/pith/MREEELXRDFYZXWCBLHCKJ554PN/action/timestamp_anchor","attest_storage":"https://pith.science/pith/MREEELXRDFYZXWCBLHCKJ554PN/action/storage_attestation","attest_author":"https://pith.science/pith/MREEELXRDFYZXWCBLHCKJ554PN/action/author_attestation","sign_citation":"https://pith.science/pith/MREEELXRDFYZXWCBLHCKJ554PN/action/citation_signature","submit_replication":"https://pith.science/pith/MREEELXRDFYZXWCBLHCKJ554PN/action/replication_record"}},"created_at":"2026-07-05T10:25:48.697623+00:00","updated_at":"2026-07-05T10:25:48.697623+00:00"}