{"record_type":"pith_number_record","schema_url":"https://pith.science/schemas/pith-number/v1.json","pith_number":"pith:2025:ONZLM4YHNSRBDP3MIKK4L4ETHT","short_pith_number":"pith:ONZLM4YH","schema_version":"1.0","canonical_sha256":"7372b673076ca211bf6c4295c5f0933cc3e89c719f408eca1cdf7799c0ddb903","source":{"kind":"arxiv","id":"2502.14591","version":1},"attestation_state":"computed","paper":{"title":"Data-driven Control of T-Product-based Dynamical Systems","license":"http://creativecommons.org/licenses/by/4.0/","headline":"","cross_cats":["cs.SY"],"primary_cat":"eess.SY","authors_text":"Anqi Dong, Can Chen, Ren Wang, Shenghan Mei, Xin Mao, Yidan Mei, Ziqin He","submitted_at":"2025-02-20T14:24:44Z","abstract_excerpt":"Data-driven control is a powerful tool that enables the design and implementation of control strategies directly from data without explicitly identifying the underlying system dynamics. While various data-driven control techniques, such as stabilization, linear quadratic regulation, and model predictive control, have been extensively developed, these methods are not inherently suited for multi-linear dynamical systems, where the states are represented as higher-order tensors. In this article, we propose a novel framework for data-driven control of T-product-based dynamical systems (TPDSs), whe"},"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":"2502.14591","kind":"arxiv","version":1},"metadata":{"license":"http://creativecommons.org/licenses/by/4.0/","primary_cat":"eess.SY","submitted_at":"2025-02-20T14:24:44Z","cross_cats_sorted":["cs.SY"],"title_canon_sha256":"bfd365eea0bb07bd550d439977472af5a246d931bd7017a077d298d1222a1a15","abstract_canon_sha256":"3dc5ec0d6f849cec16653a44d0574d5b0302afc5967ae17d03e8e93648f6fcaa"},"schema_version":"1.0"},"receipt":{"kind":"pith_receipt","key_id":"pith-v1-2026-05","algorithm":"ed25519","signed_at":"2026-07-05T10:17:29.621630Z","signature_b64":"lPQtbCqZIo2VPT0If2fi9ZsAaTfUPsSuEDNa7ZlJQkv1kD4VjcoRrr4YVVKG/PX9UiZzbjSt2Q7eWTgw1ES6DQ==","signed_message":"canonical_sha256_bytes","builder_version":"pith-number-builder-2026-05-17-v1","receipt_version":"0.3","canonical_sha256":"7372b673076ca211bf6c4295c5f0933cc3e89c719f408eca1cdf7799c0ddb903","last_reissued_at":"2026-07-05T10:17:29.621224Z","signature_status":"signed_v1","first_computed_at":"2026-07-05T10:17:29.621224Z","public_key_fingerprint":"8d4b5ee74e4693bcd1df2446408b0d54"},"graph_snapshot":{"paper":{"title":"Data-driven Control of T-Product-based Dynamical Systems","license":"http://creativecommons.org/licenses/by/4.0/","headline":"","cross_cats":["cs.SY"],"primary_cat":"eess.SY","authors_text":"Anqi Dong, Can Chen, Ren Wang, Shenghan Mei, Xin Mao, Yidan Mei, Ziqin He","submitted_at":"2025-02-20T14:24:44Z","abstract_excerpt":"Data-driven control is a powerful tool that enables the design and implementation of control strategies directly from data without explicitly identifying the underlying system dynamics. While various data-driven control techniques, such as stabilization, linear quadratic regulation, and model predictive control, have been extensively developed, these methods are not inherently suited for multi-linear dynamical systems, where the states are represented as higher-order tensors. In this article, we propose a novel framework for data-driven control of T-product-based dynamical systems (TPDSs), whe"},"claims":{"count":0,"items":[],"snapshot_sha256":"258153158e38e3291e3d48162225fcdb2d5a3ed65a07baac614ab91432fd4f57"},"source":{"id":"2502.14591","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/2502.14591/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":"2502.14591","created_at":"2026-07-05T10:17:29.621278+00:00"},{"alias_kind":"arxiv_version","alias_value":"2502.14591v1","created_at":"2026-07-05T10:17:29.621278+00:00"},{"alias_kind":"doi","alias_value":"10.48550/arxiv.2502.14591","created_at":"2026-07-05T10:17:29.621278+00:00"},{"alias_kind":"pith_short_12","alias_value":"ONZLM4YHNSRB","created_at":"2026-07-05T10:17:29.621278+00:00"},{"alias_kind":"pith_short_16","alias_value":"ONZLM4YHNSRBDP3M","created_at":"2026-07-05T10:17:29.621278+00:00"},{"alias_kind":"pith_short_8","alias_value":"ONZLM4YH","created_at":"2026-07-05T10:17:29.621278+00:00"}],"events":[],"event_summary":{},"paper_claims":[],"inbound_citations":{"count":1,"internal_anchor_count":1,"sample":[{"citing_arxiv_id":"2508.02633","citing_title":"Neural subspaces, minimax entropy, and mean-field theory for networks of neurons","ref_index":18,"is_internal_anchor":true}]},"formal_canon":{"evidence_count":0,"sample":[],"anchors":[]},"links":{"html":"https://pith.science/pith/ONZLM4YHNSRBDP3MIKK4L4ETHT","json":"https://pith.science/pith/ONZLM4YHNSRBDP3MIKK4L4ETHT.json","graph_json":"https://pith.science/api/pith-number/ONZLM4YHNSRBDP3MIKK4L4ETHT/graph.json","events_json":"https://pith.science/api/pith-number/ONZLM4YHNSRBDP3MIKK4L4ETHT/events.json","paper":"https://pith.science/paper/ONZLM4YH"},"agent_actions":{"view_html":"https://pith.science/pith/ONZLM4YHNSRBDP3MIKK4L4ETHT","download_json":"https://pith.science/pith/ONZLM4YHNSRBDP3MIKK4L4ETHT.json","view_paper":"https://pith.science/paper/ONZLM4YH","resolve_alias":"https://pith.science/api/pith-number/resolve?arxiv=2502.14591&json=true","fetch_graph":"https://pith.science/api/pith-number/ONZLM4YHNSRBDP3MIKK4L4ETHT/graph.json","fetch_events":"https://pith.science/api/pith-number/ONZLM4YHNSRBDP3MIKK4L4ETHT/events.json","actions":{"anchor_timestamp":"https://pith.science/pith/ONZLM4YHNSRBDP3MIKK4L4ETHT/action/timestamp_anchor","attest_storage":"https://pith.science/pith/ONZLM4YHNSRBDP3MIKK4L4ETHT/action/storage_attestation","attest_author":"https://pith.science/pith/ONZLM4YHNSRBDP3MIKK4L4ETHT/action/author_attestation","sign_citation":"https://pith.science/pith/ONZLM4YHNSRBDP3MIKK4L4ETHT/action/citation_signature","submit_replication":"https://pith.science/pith/ONZLM4YHNSRBDP3MIKK4L4ETHT/action/replication_record"}},"created_at":"2026-07-05T10:17:29.621278+00:00","updated_at":"2026-07-05T10:17:29.621278+00:00"}