{"record_type":"pith_number_record","schema_url":"https://pith.science/schemas/pith-number/v1.json","pith_number":"pith:2022:6LWWX5JRUP22MBKB64UEYNKYLX","short_pith_number":"pith:6LWWX5JR","schema_version":"1.0","canonical_sha256":"f2ed6bf531a3f5a60541f7284c35585de490d757acf2942345d8903869d353b5","source":{"kind":"arxiv","id":"2212.03857","version":2},"attestation_state":"computed","paper":{"title":"Phase2vec: Dynamical systems embedding with a physics-informed convolutional network","license":"http://arxiv.org/licenses/nonexclusive-distrib/1.0/","headline":"","cross_cats":["math.DS","stat.ML"],"primary_cat":"cs.LG","authors_text":"Matthew Ricci, Mor Nitzan, Noa Moriel, Zoe Piran","submitted_at":"2022-12-07T18:54:52Z","abstract_excerpt":"Dynamical systems are found in innumerable forms across the physical and biological sciences, yet all these systems fall naturally into universal equivalence classes: conservative or dissipative, stable or unstable, compressible or incompressible. Predicting these classes from data remains an essential open challenge in computational physics at which existing time-series classification methods struggle. Here, we propose, \\texttt{phase2vec}, an embedding method that learns high-quality, physically-meaningful representations of 2D dynamical systems without supervision. Our embeddings are produce"},"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":"2212.03857","kind":"arxiv","version":2},"metadata":{"license":"http://arxiv.org/licenses/nonexclusive-distrib/1.0/","primary_cat":"cs.LG","submitted_at":"2022-12-07T18:54:52Z","cross_cats_sorted":["math.DS","stat.ML"],"title_canon_sha256":"80733f4712a2cac5a17d046104b441386610d022272ae5e192147aebad7603d0","abstract_canon_sha256":"b758c8fdcaf62bdf096185966d209916cbc787f0adfc76a608c4b36069b13c7c"},"schema_version":"1.0"},"receipt":{"kind":"pith_receipt","key_id":"pith-v1-2026-05","algorithm":"ed25519","signed_at":"2026-07-05T05:45:35.870876Z","signature_b64":"X7PX4Q0VqTYbOJEdOt+x0xOxE15OjDN4JC+EJ1RDQiQQ03JwU61s6nNWywp0Nxuyjt2Z5sx5D6tJ+oY4KI65AQ==","signed_message":"canonical_sha256_bytes","builder_version":"pith-number-builder-2026-05-17-v1","receipt_version":"0.3","canonical_sha256":"f2ed6bf531a3f5a60541f7284c35585de490d757acf2942345d8903869d353b5","last_reissued_at":"2026-07-05T05:45:35.870507Z","signature_status":"signed_v1","first_computed_at":"2026-07-05T05:45:35.870507Z","public_key_fingerprint":"8d4b5ee74e4693bcd1df2446408b0d54"},"graph_snapshot":{"paper":{"title":"Phase2vec: Dynamical systems embedding with a physics-informed convolutional network","license":"http://arxiv.org/licenses/nonexclusive-distrib/1.0/","headline":"","cross_cats":["math.DS","stat.ML"],"primary_cat":"cs.LG","authors_text":"Matthew Ricci, Mor Nitzan, Noa Moriel, Zoe Piran","submitted_at":"2022-12-07T18:54:52Z","abstract_excerpt":"Dynamical systems are found in innumerable forms across the physical and biological sciences, yet all these systems fall naturally into universal equivalence classes: conservative or dissipative, stable or unstable, compressible or incompressible. Predicting these classes from data remains an essential open challenge in computational physics at which existing time-series classification methods struggle. Here, we propose, \\texttt{phase2vec}, an embedding method that learns high-quality, physically-meaningful representations of 2D dynamical systems without supervision. Our embeddings are produce"},"claims":{"count":0,"items":[],"snapshot_sha256":"258153158e38e3291e3d48162225fcdb2d5a3ed65a07baac614ab91432fd4f57"},"source":{"id":"2212.03857","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/2212.03857/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":"2212.03857","created_at":"2026-07-05T05:45:35.870572+00:00"},{"alias_kind":"arxiv_version","alias_value":"2212.03857v2","created_at":"2026-07-05T05:45:35.870572+00:00"},{"alias_kind":"doi","alias_value":"10.48550/arxiv.2212.03857","created_at":"2026-07-05T05:45:35.870572+00:00"},{"alias_kind":"pith_short_12","alias_value":"6LWWX5JRUP22","created_at":"2026-07-05T05:45:35.870572+00:00"},{"alias_kind":"pith_short_16","alias_value":"6LWWX5JRUP22MBKB","created_at":"2026-07-05T05:45:35.870572+00:00"},{"alias_kind":"pith_short_8","alias_value":"6LWWX5JR","created_at":"2026-07-05T05:45:35.870572+00:00"}],"events":[],"event_summary":{},"paper_claims":[],"inbound_citations":{"count":1,"internal_anchor_count":1,"sample":[{"citing_arxiv_id":"2607.09811","citing_title":"Detangled: A Framework for Creating, Editing, and Inferencing Feature Rich Hair Strands","ref_index":114,"is_internal_anchor":true}]},"formal_canon":{"evidence_count":0,"sample":[],"anchors":[]},"links":{"html":"https://pith.science/pith/6LWWX5JRUP22MBKB64UEYNKYLX","json":"https://pith.science/pith/6LWWX5JRUP22MBKB64UEYNKYLX.json","graph_json":"https://pith.science/api/pith-number/6LWWX5JRUP22MBKB64UEYNKYLX/graph.json","events_json":"https://pith.science/api/pith-number/6LWWX5JRUP22MBKB64UEYNKYLX/events.json","paper":"https://pith.science/paper/6LWWX5JR"},"agent_actions":{"view_html":"https://pith.science/pith/6LWWX5JRUP22MBKB64UEYNKYLX","download_json":"https://pith.science/pith/6LWWX5JRUP22MBKB64UEYNKYLX.json","view_paper":"https://pith.science/paper/6LWWX5JR","resolve_alias":"https://pith.science/api/pith-number/resolve?arxiv=2212.03857&json=true","fetch_graph":"https://pith.science/api/pith-number/6LWWX5JRUP22MBKB64UEYNKYLX/graph.json","fetch_events":"https://pith.science/api/pith-number/6LWWX5JRUP22MBKB64UEYNKYLX/events.json","actions":{"anchor_timestamp":"https://pith.science/pith/6LWWX5JRUP22MBKB64UEYNKYLX/action/timestamp_anchor","attest_storage":"https://pith.science/pith/6LWWX5JRUP22MBKB64UEYNKYLX/action/storage_attestation","attest_author":"https://pith.science/pith/6LWWX5JRUP22MBKB64UEYNKYLX/action/author_attestation","sign_citation":"https://pith.science/pith/6LWWX5JRUP22MBKB64UEYNKYLX/action/citation_signature","submit_replication":"https://pith.science/pith/6LWWX5JRUP22MBKB64UEYNKYLX/action/replication_record"}},"created_at":"2026-07-05T05:45:35.870572+00:00","updated_at":"2026-07-05T05:45:35.870572+00:00"}