{"record_type":"pith_number_record","schema_url":"https://pith.science/schemas/pith-number/v1.json","pith_number":"pith:2024:GYQRTO7UOBRS3VHFYQJLKNJUWZ","short_pith_number":"pith:GYQRTO7U","schema_version":"1.0","canonical_sha256":"362119bbf470632dd4e5c412b53534b671cd4ac95058cfbd0b1096b52ad9c4d7","source":{"kind":"arxiv","id":"2410.05281","version":1},"attestation_state":"computed","paper":{"title":"Micrometer: Micromechanics Transformer for Predicting Mechanical Responses of Heterogeneous Materials","license":"http://creativecommons.org/licenses/by-nc-nd/4.0/","headline":"","cross_cats":["cond-mat.mtrl-sci","cs.LG","physics.comp-ph"],"primary_cat":"cs.CE","authors_text":"Paris Perdikaris, Shyam Sankaran, Sifan Wang, Tong-Rui Liu","submitted_at":"2024-09-23T16:01:37Z","abstract_excerpt":"Heterogeneous materials, crucial in various engineering applications, exhibit complex multiscale behavior, which challenges the effectiveness of traditional computational methods. In this work, we introduce the Micromechanics Transformer ({\\em Micrometer}), an artificial intelligence (AI) framework for predicting the mechanical response of heterogeneous materials, bridging the gap between advanced data-driven methods and complex solid mechanics problems. Trained on a large-scale high-resolution dataset of 2D fiber-reinforced composites, Micrometer can achieve state-of-the-art performance in pr"},"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":"2410.05281","kind":"arxiv","version":1},"metadata":{"license":"http://creativecommons.org/licenses/by-nc-nd/4.0/","primary_cat":"cs.CE","submitted_at":"2024-09-23T16:01:37Z","cross_cats_sorted":["cond-mat.mtrl-sci","cs.LG","physics.comp-ph"],"title_canon_sha256":"1149b689dec3baf061655a2eebaa5d6f0a23980f2864dca6dc604fb313cf3f68","abstract_canon_sha256":"e376cc1aa0595e8ffeb6817d5b879c62c57bcc25e7cad9b3e4c655b224615a1a"},"schema_version":"1.0"},"receipt":{"kind":"pith_receipt","key_id":"pith-v1-2026-05","algorithm":"ed25519","signed_at":"2026-07-05T09:17:16.673519Z","signature_b64":"aLoaGhbmU75fq6lzGpQAbHq0B/YpfsrbLeW/mKAsYSB3ydSFfCsEHVyPtgEyLFllyfbWNZGGkE/RV7VSK7TdDQ==","signed_message":"canonical_sha256_bytes","builder_version":"pith-number-builder-2026-05-17-v1","receipt_version":"0.3","canonical_sha256":"362119bbf470632dd4e5c412b53534b671cd4ac95058cfbd0b1096b52ad9c4d7","last_reissued_at":"2026-07-05T09:17:16.673045Z","signature_status":"signed_v1","first_computed_at":"2026-07-05T09:17:16.673045Z","public_key_fingerprint":"8d4b5ee74e4693bcd1df2446408b0d54"},"graph_snapshot":{"paper":{"title":"Micrometer: Micromechanics Transformer for Predicting Mechanical Responses of Heterogeneous Materials","license":"http://creativecommons.org/licenses/by-nc-nd/4.0/","headline":"","cross_cats":["cond-mat.mtrl-sci","cs.LG","physics.comp-ph"],"primary_cat":"cs.CE","authors_text":"Paris Perdikaris, Shyam Sankaran, Sifan Wang, Tong-Rui Liu","submitted_at":"2024-09-23T16:01:37Z","abstract_excerpt":"Heterogeneous materials, crucial in various engineering applications, exhibit complex multiscale behavior, which challenges the effectiveness of traditional computational methods. In this work, we introduce the Micromechanics Transformer ({\\em Micrometer}), an artificial intelligence (AI) framework for predicting the mechanical response of heterogeneous materials, bridging the gap between advanced data-driven methods and complex solid mechanics problems. Trained on a large-scale high-resolution dataset of 2D fiber-reinforced composites, Micrometer can achieve state-of-the-art performance in pr"},"claims":{"count":0,"items":[],"snapshot_sha256":"258153158e38e3291e3d48162225fcdb2d5a3ed65a07baac614ab91432fd4f57"},"source":{"id":"2410.05281","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/2410.05281/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":"2410.05281","created_at":"2026-07-05T09:17:16.673101+00:00"},{"alias_kind":"arxiv_version","alias_value":"2410.05281v1","created_at":"2026-07-05T09:17:16.673101+00:00"},{"alias_kind":"doi","alias_value":"10.48550/arxiv.2410.05281","created_at":"2026-07-05T09:17:16.673101+00:00"},{"alias_kind":"pith_short_12","alias_value":"GYQRTO7UOBRS","created_at":"2026-07-05T09:17:16.673101+00:00"},{"alias_kind":"pith_short_16","alias_value":"GYQRTO7UOBRS3VHF","created_at":"2026-07-05T09:17:16.673101+00:00"},{"alias_kind":"pith_short_8","alias_value":"GYQRTO7U","created_at":"2026-07-05T09:17:16.673101+00:00"}],"events":[],"event_summary":{},"paper_claims":[],"inbound_citations":{"count":1,"internal_anchor_count":0,"sample":[{"citing_arxiv_id":"2605.25949","citing_title":"Small Models, Strong Priors: Architectural Inductive Bias for Parameter-Efficient Neural PDE Solvers","ref_index":45,"is_internal_anchor":false}]},"formal_canon":{"evidence_count":0,"sample":[],"anchors":[]},"links":{"html":"https://pith.science/pith/GYQRTO7UOBRS3VHFYQJLKNJUWZ","json":"https://pith.science/pith/GYQRTO7UOBRS3VHFYQJLKNJUWZ.json","graph_json":"https://pith.science/api/pith-number/GYQRTO7UOBRS3VHFYQJLKNJUWZ/graph.json","events_json":"https://pith.science/api/pith-number/GYQRTO7UOBRS3VHFYQJLKNJUWZ/events.json","paper":"https://pith.science/paper/GYQRTO7U"},"agent_actions":{"view_html":"https://pith.science/pith/GYQRTO7UOBRS3VHFYQJLKNJUWZ","download_json":"https://pith.science/pith/GYQRTO7UOBRS3VHFYQJLKNJUWZ.json","view_paper":"https://pith.science/paper/GYQRTO7U","resolve_alias":"https://pith.science/api/pith-number/resolve?arxiv=2410.05281&json=true","fetch_graph":"https://pith.science/api/pith-number/GYQRTO7UOBRS3VHFYQJLKNJUWZ/graph.json","fetch_events":"https://pith.science/api/pith-number/GYQRTO7UOBRS3VHFYQJLKNJUWZ/events.json","actions":{"anchor_timestamp":"https://pith.science/pith/GYQRTO7UOBRS3VHFYQJLKNJUWZ/action/timestamp_anchor","attest_storage":"https://pith.science/pith/GYQRTO7UOBRS3VHFYQJLKNJUWZ/action/storage_attestation","attest_author":"https://pith.science/pith/GYQRTO7UOBRS3VHFYQJLKNJUWZ/action/author_attestation","sign_citation":"https://pith.science/pith/GYQRTO7UOBRS3VHFYQJLKNJUWZ/action/citation_signature","submit_replication":"https://pith.science/pith/GYQRTO7UOBRS3VHFYQJLKNJUWZ/action/replication_record"}},"created_at":"2026-07-05T09:17:16.673101+00:00","updated_at":"2026-07-05T09:17:16.673101+00:00"}