{"record_type":"pith_number_record","schema_url":"https://pith.science/schemas/pith-number/v1.json","pith_number":"pith:2024:4FQZUN6EFZNHKZGGY4AUVR7OVH","short_pith_number":"pith:4FQZUN6E","schema_version":"1.0","canonical_sha256":"e1619a37c42e5a7564c6c7014ac7eea9e81f7623c21cc0bcaa3b77905ef933ff","source":{"kind":"arxiv","id":"2403.08448","version":2},"attestation_state":"computed","paper":{"title":"Actor-Critic Physics-informed Neural Lyapunov Control","license":"http://arxiv.org/licenses/nonexclusive-distrib/1.0/","headline":"","cross_cats":["cs.RO","cs.SY","eess.SY"],"primary_cat":"cs.LG","authors_text":"Jiarui Wang, Mahyar Fazlyab","submitted_at":"2024-03-13T12:03:27Z","abstract_excerpt":"Designing control policies for stabilization tasks with provable guarantees is a long-standing problem in nonlinear control. A crucial performance metric is the size of the resulting region of attraction, which essentially serves as a robustness \"margin\" of the closed-loop system against uncertainties. In this paper, we propose a new method to train a stabilizing neural network controller along with its corresponding Lyapunov certificate, aiming to maximize the resulting region of attraction while respecting the actuation constraints. Crucial to our approach is the use of Zubov's Partial Diffe"},"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":"2403.08448","kind":"arxiv","version":2},"metadata":{"license":"http://arxiv.org/licenses/nonexclusive-distrib/1.0/","primary_cat":"cs.LG","submitted_at":"2024-03-13T12:03:27Z","cross_cats_sorted":["cs.RO","cs.SY","eess.SY"],"title_canon_sha256":"435f2ae7a784c641cd0ac0457695ff696848c8c2171e813ca50281573184f277","abstract_canon_sha256":"5e64f83a9853477c567f7b00a2b907834cfb30c1b9f8ccfea39ade401eb06f31"},"schema_version":"1.0"},"receipt":{"kind":"pith_receipt","key_id":"pith-v1-2026-05","algorithm":"ed25519","signed_at":"2026-07-05T08:50:54.654000Z","signature_b64":"l2uKUpvV74zxrKGULzuT0QZ/G6oNQA2MdakClY7nAsKjP393GSSICIOA7eazKQ1YCh4q2NvBO/m8nZyudslFAQ==","signed_message":"canonical_sha256_bytes","builder_version":"pith-number-builder-2026-05-17-v1","receipt_version":"0.3","canonical_sha256":"e1619a37c42e5a7564c6c7014ac7eea9e81f7623c21cc0bcaa3b77905ef933ff","last_reissued_at":"2026-07-05T08:50:54.653525Z","signature_status":"signed_v1","first_computed_at":"2026-07-05T08:50:54.653525Z","public_key_fingerprint":"8d4b5ee74e4693bcd1df2446408b0d54"},"graph_snapshot":{"paper":{"title":"Actor-Critic Physics-informed Neural Lyapunov Control","license":"http://arxiv.org/licenses/nonexclusive-distrib/1.0/","headline":"","cross_cats":["cs.RO","cs.SY","eess.SY"],"primary_cat":"cs.LG","authors_text":"Jiarui Wang, Mahyar Fazlyab","submitted_at":"2024-03-13T12:03:27Z","abstract_excerpt":"Designing control policies for stabilization tasks with provable guarantees is a long-standing problem in nonlinear control. A crucial performance metric is the size of the resulting region of attraction, which essentially serves as a robustness \"margin\" of the closed-loop system against uncertainties. In this paper, we propose a new method to train a stabilizing neural network controller along with its corresponding Lyapunov certificate, aiming to maximize the resulting region of attraction while respecting the actuation constraints. Crucial to our approach is the use of Zubov's Partial Diffe"},"claims":{"count":0,"items":[],"snapshot_sha256":"258153158e38e3291e3d48162225fcdb2d5a3ed65a07baac614ab91432fd4f57"},"source":{"id":"2403.08448","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/2403.08448/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":"2403.08448","created_at":"2026-07-05T08:50:54.653580+00:00"},{"alias_kind":"arxiv_version","alias_value":"2403.08448v2","created_at":"2026-07-05T08:50:54.653580+00:00"},{"alias_kind":"doi","alias_value":"10.48550/arxiv.2403.08448","created_at":"2026-07-05T08:50:54.653580+00:00"},{"alias_kind":"pith_short_12","alias_value":"4FQZUN6EFZNH","created_at":"2026-07-05T08:50:54.653580+00:00"},{"alias_kind":"pith_short_16","alias_value":"4FQZUN6EFZNHKZGG","created_at":"2026-07-05T08:50:54.653580+00:00"},{"alias_kind":"pith_short_8","alias_value":"4FQZUN6E","created_at":"2026-07-05T08:50:54.653580+00:00"}],"events":[],"event_summary":{},"paper_claims":[],"inbound_citations":{"count":1,"internal_anchor_count":0,"sample":[{"citing_arxiv_id":"2508.09128","citing_title":"A Review On Safe Reinforcement Learning Using Lyapunov and Barrier Functions","ref_index":58,"is_internal_anchor":false}]},"formal_canon":{"evidence_count":0,"sample":[],"anchors":[]},"links":{"html":"https://pith.science/pith/4FQZUN6EFZNHKZGGY4AUVR7OVH","json":"https://pith.science/pith/4FQZUN6EFZNHKZGGY4AUVR7OVH.json","graph_json":"https://pith.science/api/pith-number/4FQZUN6EFZNHKZGGY4AUVR7OVH/graph.json","events_json":"https://pith.science/api/pith-number/4FQZUN6EFZNHKZGGY4AUVR7OVH/events.json","paper":"https://pith.science/paper/4FQZUN6E"},"agent_actions":{"view_html":"https://pith.science/pith/4FQZUN6EFZNHKZGGY4AUVR7OVH","download_json":"https://pith.science/pith/4FQZUN6EFZNHKZGGY4AUVR7OVH.json","view_paper":"https://pith.science/paper/4FQZUN6E","resolve_alias":"https://pith.science/api/pith-number/resolve?arxiv=2403.08448&json=true","fetch_graph":"https://pith.science/api/pith-number/4FQZUN6EFZNHKZGGY4AUVR7OVH/graph.json","fetch_events":"https://pith.science/api/pith-number/4FQZUN6EFZNHKZGGY4AUVR7OVH/events.json","actions":{"anchor_timestamp":"https://pith.science/pith/4FQZUN6EFZNHKZGGY4AUVR7OVH/action/timestamp_anchor","attest_storage":"https://pith.science/pith/4FQZUN6EFZNHKZGGY4AUVR7OVH/action/storage_attestation","attest_author":"https://pith.science/pith/4FQZUN6EFZNHKZGGY4AUVR7OVH/action/author_attestation","sign_citation":"https://pith.science/pith/4FQZUN6EFZNHKZGGY4AUVR7OVH/action/citation_signature","submit_replication":"https://pith.science/pith/4FQZUN6EFZNHKZGGY4AUVR7OVH/action/replication_record"}},"created_at":"2026-07-05T08:50:54.653580+00:00","updated_at":"2026-07-05T08:50:54.653580+00:00"}