{"record_type":"pith_number_record","schema_url":"https://pith.science/schemas/pith-number/v1.json","pith_number":"pith:2024:OPXPCQNDXJVUQUDRCRSFP7QWKM","short_pith_number":"pith:OPXPCQND","schema_version":"1.0","canonical_sha256":"73eef141a3ba6b485071146457fe16530723d2c2bdf60abe3257414439bdc369","source":{"kind":"arxiv","id":"2406.02875","version":3},"attestation_state":"computed","paper":{"title":"Leveraging KANs For Enhanced Deep Koopman Operator Discovery","license":"http://creativecommons.org/licenses/by/4.0/","headline":"","cross_cats":["math.DS","physics.app-ph","physics.comp-ph"],"primary_cat":"cs.LG","authors_text":"George Nehma, Madhur Tiwari","submitted_at":"2024-06-05T02:50:27Z","abstract_excerpt":"Multi-layer perceptrons (MLP's) have been extensively utilized in discovering Deep Koopman operators for linearizing nonlinear dynamics. With the emergence of Kolmogorov-Arnold Networks (KANs) as a more efficient and accurate alternative to the MLP Neural Network, we propose a comparison of the performance of each network type in the context of learning Koopman operators with control. In this work, we propose a KANs-based deep Koopman framework with applications to an orbital Two-Body Problem (2BP) and the pendulum for data-driven discovery of linear system dynamics. KANs were found to be supe"},"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":"2406.02875","kind":"arxiv","version":3},"metadata":{"license":"http://creativecommons.org/licenses/by/4.0/","primary_cat":"cs.LG","submitted_at":"2024-06-05T02:50:27Z","cross_cats_sorted":["math.DS","physics.app-ph","physics.comp-ph"],"title_canon_sha256":"54ac364ce1232dbfc3a3e1dcb1cfe1d59e253a290ef1905b643c77871c8b6921","abstract_canon_sha256":"67568b86839e782feb656b3ed98995484a318bd317b8b38ae2717d4e430e6285"},"schema_version":"1.0"},"receipt":{"kind":"pith_receipt","key_id":"pith-v1-2026-05","algorithm":"ed25519","signed_at":"2026-07-05T08:54:36.433159Z","signature_b64":"2ufgQaasjKlewp+XGhjMcsKGrtyoDrFuOMtPq9cvrMINbeWaTPVXxu1BE4rwrmAJL+GVD2Xjw6TouUl85UcgBg==","signed_message":"canonical_sha256_bytes","builder_version":"pith-number-builder-2026-05-17-v1","receipt_version":"0.3","canonical_sha256":"73eef141a3ba6b485071146457fe16530723d2c2bdf60abe3257414439bdc369","last_reissued_at":"2026-07-05T08:54:36.432743Z","signature_status":"signed_v1","first_computed_at":"2026-07-05T08:54:36.432743Z","public_key_fingerprint":"8d4b5ee74e4693bcd1df2446408b0d54"},"graph_snapshot":{"paper":{"title":"Leveraging KANs For Enhanced Deep Koopman Operator Discovery","license":"http://creativecommons.org/licenses/by/4.0/","headline":"","cross_cats":["math.DS","physics.app-ph","physics.comp-ph"],"primary_cat":"cs.LG","authors_text":"George Nehma, Madhur Tiwari","submitted_at":"2024-06-05T02:50:27Z","abstract_excerpt":"Multi-layer perceptrons (MLP's) have been extensively utilized in discovering Deep Koopman operators for linearizing nonlinear dynamics. With the emergence of Kolmogorov-Arnold Networks (KANs) as a more efficient and accurate alternative to the MLP Neural Network, we propose a comparison of the performance of each network type in the context of learning Koopman operators with control. In this work, we propose a KANs-based deep Koopman framework with applications to an orbital Two-Body Problem (2BP) and the pendulum for data-driven discovery of linear system dynamics. KANs were found to be supe"},"claims":{"count":0,"items":[],"snapshot_sha256":"258153158e38e3291e3d48162225fcdb2d5a3ed65a07baac614ab91432fd4f57"},"source":{"id":"2406.02875","kind":"arxiv","version":3},"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/2406.02875/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":"2406.02875","created_at":"2026-07-05T08:54:36.432800+00:00"},{"alias_kind":"arxiv_version","alias_value":"2406.02875v3","created_at":"2026-07-05T08:54:36.432800+00:00"},{"alias_kind":"doi","alias_value":"10.48550/arxiv.2406.02875","created_at":"2026-07-05T08:54:36.432800+00:00"},{"alias_kind":"pith_short_12","alias_value":"OPXPCQNDXJVU","created_at":"2026-07-05T08:54:36.432800+00:00"},{"alias_kind":"pith_short_16","alias_value":"OPXPCQNDXJVUQUDR","created_at":"2026-07-05T08:54:36.432800+00:00"},{"alias_kind":"pith_short_8","alias_value":"OPXPCQND","created_at":"2026-07-05T08:54:36.432800+00:00"}],"events":[],"event_summary":{},"paper_claims":[],"inbound_citations":{"count":1,"internal_anchor_count":0,"sample":[{"citing_arxiv_id":"2604.16842","citing_title":"Singularity Formation: Synergy in Theoretical, Numerical and Machine Learning Approaches","ref_index":271,"is_internal_anchor":false}]},"formal_canon":{"evidence_count":0,"sample":[],"anchors":[]},"links":{"html":"https://pith.science/pith/OPXPCQNDXJVUQUDRCRSFP7QWKM","json":"https://pith.science/pith/OPXPCQNDXJVUQUDRCRSFP7QWKM.json","graph_json":"https://pith.science/api/pith-number/OPXPCQNDXJVUQUDRCRSFP7QWKM/graph.json","events_json":"https://pith.science/api/pith-number/OPXPCQNDXJVUQUDRCRSFP7QWKM/events.json","paper":"https://pith.science/paper/OPXPCQND"},"agent_actions":{"view_html":"https://pith.science/pith/OPXPCQNDXJVUQUDRCRSFP7QWKM","download_json":"https://pith.science/pith/OPXPCQNDXJVUQUDRCRSFP7QWKM.json","view_paper":"https://pith.science/paper/OPXPCQND","resolve_alias":"https://pith.science/api/pith-number/resolve?arxiv=2406.02875&json=true","fetch_graph":"https://pith.science/api/pith-number/OPXPCQNDXJVUQUDRCRSFP7QWKM/graph.json","fetch_events":"https://pith.science/api/pith-number/OPXPCQNDXJVUQUDRCRSFP7QWKM/events.json","actions":{"anchor_timestamp":"https://pith.science/pith/OPXPCQNDXJVUQUDRCRSFP7QWKM/action/timestamp_anchor","attest_storage":"https://pith.science/pith/OPXPCQNDXJVUQUDRCRSFP7QWKM/action/storage_attestation","attest_author":"https://pith.science/pith/OPXPCQNDXJVUQUDRCRSFP7QWKM/action/author_attestation","sign_citation":"https://pith.science/pith/OPXPCQNDXJVUQUDRCRSFP7QWKM/action/citation_signature","submit_replication":"https://pith.science/pith/OPXPCQNDXJVUQUDRCRSFP7QWKM/action/replication_record"}},"created_at":"2026-07-05T08:54:36.432800+00:00","updated_at":"2026-07-05T08:54:36.432800+00:00"}