{"record_type":"pith_number_record","schema_url":"https://pith.science/schemas/pith-number/v1.json","pith_number":"pith:2026:RCTA5IJOFP4IHIMHILGNFNZQYI","short_pith_number":"pith:RCTA5IJO","schema_version":"1.0","canonical_sha256":"88a60ea12e2bf883a18742ccd2b730c213acbf67cbb6735d75902948830d516f","source":{"kind":"arxiv","id":"2607.20535","version":1},"attestation_state":"computed","paper":{"title":"A Graph Neural Network approach to zero-shot Digital Twins","license":"http://creativecommons.org/licenses/by-nc-sa/4.0/","headline":"","cross_cats":["cs.AI","cs.CV"],"primary_cat":"cs.LG","authors_text":"Alicia Tierz, David Gonz\\'alez, El\\'ias Cueto, Ic\\'iar Alfaro","submitted_at":"2026-07-10T16:04:58Z","abstract_excerpt":"Traditional Predictive Digital Twins often remain geometrically rigid, requiring extensive retraining or fine-tuning whenever the underlying physical domain or boundary conditions change. To overcome this limitation, we present a novel framework for \\textit{Zero-Shot Digital Twins} that seamlessly couples real-time visual perception with a geometry-agnostic, physics-informed reasoning engine. At the core of our architecture is the Thermodynamics-Informed Graph Neural Network architecture, a Geometric Deep Learning solver grounded in a metriplectic thermodynamic formalism that enforces energy c"},"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":"2607.20535","kind":"arxiv","version":1},"metadata":{"license":"http://creativecommons.org/licenses/by-nc-sa/4.0/","primary_cat":"cs.LG","submitted_at":"2026-07-10T16:04:58Z","cross_cats_sorted":["cs.AI","cs.CV"],"title_canon_sha256":"d14409d9bac2a4bee2b306e770c7e92d03e7394cc6052d1236ffe9a03a2b449c","abstract_canon_sha256":"ab11df9b3c746418e1ee7f55f9156d9b137c57374cd927e27f8fa28f2dfc05cc"},"schema_version":"1.0"},"receipt":{"kind":"pith_receipt","key_id":"pith-v1-2026-05","algorithm":"ed25519","signed_at":"2026-07-24T00:23:21.092994Z","signature_b64":"xCsImvypLS+lMJHASeSSo1wZTBxzo2WtL0tduQ2XAqGCu2Zpq+N3PFkQ9BKp1d7Br2K2HTx0NvIK12V8u+HMBQ==","signed_message":"canonical_sha256_bytes","builder_version":"pith-number-builder-2026-05-17-v1","receipt_version":"0.3","canonical_sha256":"88a60ea12e2bf883a18742ccd2b730c213acbf67cbb6735d75902948830d516f","last_reissued_at":"2026-07-24T00:23:21.092109Z","signature_status":"signed_v1","first_computed_at":"2026-07-24T00:23:21.092109Z","public_key_fingerprint":"8d4b5ee74e4693bcd1df2446408b0d54"},"graph_snapshot":{"paper":{"title":"A Graph Neural Network approach to zero-shot Digital Twins","license":"http://creativecommons.org/licenses/by-nc-sa/4.0/","headline":"","cross_cats":["cs.AI","cs.CV"],"primary_cat":"cs.LG","authors_text":"Alicia Tierz, David Gonz\\'alez, El\\'ias Cueto, Ic\\'iar Alfaro","submitted_at":"2026-07-10T16:04:58Z","abstract_excerpt":"Traditional Predictive Digital Twins often remain geometrically rigid, requiring extensive retraining or fine-tuning whenever the underlying physical domain or boundary conditions change. To overcome this limitation, we present a novel framework for \\textit{Zero-Shot Digital Twins} that seamlessly couples real-time visual perception with a geometry-agnostic, physics-informed reasoning engine. At the core of our architecture is the Thermodynamics-Informed Graph Neural Network architecture, a Geometric Deep Learning solver grounded in a metriplectic thermodynamic formalism that enforces energy c"},"claims":{"count":0,"items":[],"snapshot_sha256":"258153158e38e3291e3d48162225fcdb2d5a3ed65a07baac614ab91432fd4f57"},"source":{"id":"2607.20535","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/2607.20535/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":"2607.20535","created_at":"2026-07-24T00:23:21.092576+00:00"},{"alias_kind":"arxiv_version","alias_value":"2607.20535v1","created_at":"2026-07-24T00:23:21.092576+00:00"},{"alias_kind":"doi","alias_value":"10.48550/arxiv.2607.20535","created_at":"2026-07-24T00:23:21.092576+00:00"},{"alias_kind":"pith_short_12","alias_value":"RCTA5IJOFP4I","created_at":"2026-07-24T00:23:21.092576+00:00"},{"alias_kind":"pith_short_16","alias_value":"RCTA5IJOFP4IHIMH","created_at":"2026-07-24T00:23:21.092576+00:00"},{"alias_kind":"pith_short_8","alias_value":"RCTA5IJO","created_at":"2026-07-24T00:23:21.092576+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/RCTA5IJOFP4IHIMHILGNFNZQYI","json":"https://pith.science/pith/RCTA5IJOFP4IHIMHILGNFNZQYI.json","graph_json":"https://pith.science/api/pith-number/RCTA5IJOFP4IHIMHILGNFNZQYI/graph.json","events_json":"https://pith.science/api/pith-number/RCTA5IJOFP4IHIMHILGNFNZQYI/events.json","paper":"https://pith.science/paper/RCTA5IJO"},"agent_actions":{"view_html":"https://pith.science/pith/RCTA5IJOFP4IHIMHILGNFNZQYI","download_json":"https://pith.science/pith/RCTA5IJOFP4IHIMHILGNFNZQYI.json","view_paper":"https://pith.science/paper/RCTA5IJO","resolve_alias":"https://pith.science/api/pith-number/resolve?arxiv=2607.20535&json=true","fetch_graph":"https://pith.science/api/pith-number/RCTA5IJOFP4IHIMHILGNFNZQYI/graph.json","fetch_events":"https://pith.science/api/pith-number/RCTA5IJOFP4IHIMHILGNFNZQYI/events.json","actions":{"anchor_timestamp":"https://pith.science/pith/RCTA5IJOFP4IHIMHILGNFNZQYI/action/timestamp_anchor","attest_storage":"https://pith.science/pith/RCTA5IJOFP4IHIMHILGNFNZQYI/action/storage_attestation","attest_author":"https://pith.science/pith/RCTA5IJOFP4IHIMHILGNFNZQYI/action/author_attestation","sign_citation":"https://pith.science/pith/RCTA5IJOFP4IHIMHILGNFNZQYI/action/citation_signature","submit_replication":"https://pith.science/pith/RCTA5IJOFP4IHIMHILGNFNZQYI/action/replication_record"}},"created_at":"2026-07-24T00:23:21.092576+00:00","updated_at":"2026-07-24T00:23:21.092576+00:00"}