{"bundle_type":"pith_open_graph_bundle","bundle_version":"1.0","pith_number":"pith:2022:T75T5AAJG5EOMVRKB2YWKXAEEY","short_pith_number":"pith:T75T5AAJ","canonical_record":{"source":{"id":"2205.08754","kind":"arxiv","version":1},"metadata":{"license":"http://arxiv.org/licenses/nonexclusive-distrib/1.0/","primary_cat":"cs.LG","submitted_at":"2022-05-18T06:50:44Z","cross_cats_sorted":[],"title_canon_sha256":"43d3fc41fd70d8006a88025c569aa903626a3175d0b30bc4aa7aa3f1f11b7e7e","abstract_canon_sha256":"e0a1db32e7de282e5c3ee16ff3b141f68c247bffb49b60c23a3439ee86666609"},"schema_version":"1.0"},"canonical_sha256":"9ffb3e80093748e6562a0eb1655c042624df63fad1dbc58a46731bfe02cf04b9","source":{"kind":"arxiv","id":"2205.08754","version":1},"source_aliases":[{"alias_kind":"arxiv","alias_value":"2205.08754","created_at":"2026-07-05T04:24:28Z"},{"alias_kind":"arxiv_version","alias_value":"2205.08754v1","created_at":"2026-07-05T04:24:28Z"},{"alias_kind":"doi","alias_value":"10.48550/arxiv.2205.08754","created_at":"2026-07-05T04:24:28Z"},{"alias_kind":"pith_short_12","alias_value":"T75T5AAJG5EO","created_at":"2026-07-05T04:24:28Z"},{"alias_kind":"pith_short_16","alias_value":"T75T5AAJG5EOMVRK","created_at":"2026-07-05T04:24:28Z"},{"alias_kind":"pith_short_8","alias_value":"T75T5AAJ","created_at":"2026-07-05T04:24:28Z"}],"events":[{"event_type":"record_created","subject_pith_number":"pith:2022:T75T5AAJG5EOMVRKB2YWKXAEEY","target":"record","payload":{"canonical_record":{"source":{"id":"2205.08754","kind":"arxiv","version":1},"metadata":{"license":"http://arxiv.org/licenses/nonexclusive-distrib/1.0/","primary_cat":"cs.LG","submitted_at":"2022-05-18T06:50:44Z","cross_cats_sorted":[],"title_canon_sha256":"43d3fc41fd70d8006a88025c569aa903626a3175d0b30bc4aa7aa3f1f11b7e7e","abstract_canon_sha256":"e0a1db32e7de282e5c3ee16ff3b141f68c247bffb49b60c23a3439ee86666609"},"schema_version":"1.0"},"canonical_sha256":"9ffb3e80093748e6562a0eb1655c042624df63fad1dbc58a46731bfe02cf04b9","receipt":{"kind":"pith_receipt","key_id":"pith-v1-2026-05","algorithm":"ed25519","signed_at":"2026-07-05T04:24:28.804277Z","signature_b64":"icIn4qB8GK7lP569r3o091a+HiqFot5wbxfNLzjYlSPcuIYDEhShe73Q5o40CYLmCnLenWWYOpDSnHhT4eMICg==","signed_message":"canonical_sha256_bytes","builder_version":"pith-number-builder-2026-05-17-v1","receipt_version":"0.3","canonical_sha256":"9ffb3e80093748e6562a0eb1655c042624df63fad1dbc58a46731bfe02cf04b9","last_reissued_at":"2026-07-05T04:24:28.803835Z","signature_status":"signed_v1","first_computed_at":"2026-07-05T04:24:28.803835Z","public_key_fingerprint":"8d4b5ee74e4693bcd1df2446408b0d54"},"source_kind":"arxiv","source_id":"2205.08754","source_version":1,"attestation_state":"computed"},"signer":{"signer_id":"pith.science","signer_type":"pith_registry","key_id":"pith-v1-2026-05","public_key_fingerprint":"8d4b5ee74e4693bcd1df2446408b0d54"},"created_at":"2026-07-05T04:24:28Z","supersedes":[],"prev_event":null,"signature":{"signature_status":"signed_v1","algorithm":"ed25519","key_id":"pith-v1-2026-05","public_key_fingerprint":"8d4b5ee74e4693bcd1df2446408b0d54","signature_b64":"l3m1d5UCjdhlAaLK8xZlBJaHSU6YKZYERL1fqcTxCCGgGuUSqCp5klOrEV+fGAYYTii2V+qXpVyvtO+bHPnACQ==","signed_message":"open_graph_event_sha256_bytes","signed_at":"2026-07-05T14:43:29.340138Z"},"content_sha256":"d51d12dd910158ab5666ec83873a624c708b279d2a4a47c0e861b0611064a2e1","schema_version":"1.0","event_id":"sha256:d51d12dd910158ab5666ec83873a624c708b279d2a4a47c0e861b0611064a2e1"},{"event_type":"graph_snapshot","subject_pith_number":"pith:2022:T75T5AAJG5EOMVRKB2YWKXAEEY","target":"graph","payload":{"graph_snapshot":{"paper":{"title":"Revisiting PINNs: Generative Adversarial Physics-informed Neural Networks and Point-weighting Method","license":"http://arxiv.org/licenses/nonexclusive-distrib/1.0/","headline":"","cross_cats":[],"primary_cat":"cs.LG","authors_text":"Chao Zhang, Chuncheng Wang, Dacheng Tao, Hanting Guan, Wensheng Li","submitted_at":"2022-05-18T06:50:44Z","abstract_excerpt":"Physics-informed neural networks (PINNs) provide a deep learning framework for numerically solving partial differential equations (PDEs), and have been widely used in a variety of PDE problems. However, there still remain some challenges in the application of PINNs: 1) the mechanism of PINNs is unsuitable (at least cannot be directly applied) to exploiting a small size of (usually very few) extra informative samples to refine the networks; and 2) the efficiency of training PINNs often becomes low for some complicated PDEs. In this paper, we propose the generative adversarial physics-informed n"},"claims":{"count":0,"items":[],"snapshot_sha256":"258153158e38e3291e3d48162225fcdb2d5a3ed65a07baac614ab91432fd4f57"},"source":{"id":"2205.08754","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/2205.08754/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"},"verdict_id":null},"signer":{"signer_id":"pith.science","signer_type":"pith_registry","key_id":"pith-v1-2026-05","public_key_fingerprint":"8d4b5ee74e4693bcd1df2446408b0d54"},"created_at":"2026-07-05T04:24:28Z","supersedes":[],"prev_event":null,"signature":{"signature_status":"signed_v1","algorithm":"ed25519","key_id":"pith-v1-2026-05","public_key_fingerprint":"8d4b5ee74e4693bcd1df2446408b0d54","signature_b64":"+BGBlEBqF6jQNn0OUEE6XWh/q2zuUvMbe7Y5nr+A1eEAliQVOfoEJ9+xR86WwmssHq4Te2wfB79rss/oflaNBw==","signed_message":"open_graph_event_sha256_bytes","signed_at":"2026-07-05T14:43:29.340803Z"},"content_sha256":"684997299808e984919128e0760151e360fc11f97c081029ff877dbf445057fb","schema_version":"1.0","event_id":"sha256:684997299808e984919128e0760151e360fc11f97c081029ff877dbf445057fb"}],"timestamp_proofs":[],"mirror_hints":[{"mirror_type":"https","name":"Pith Resolver","base_url":"https://pith.science","bundle_url":"https://pith.science/pith/T75T5AAJG5EOMVRKB2YWKXAEEY/bundle.json","state_url":"https://pith.science/pith/T75T5AAJG5EOMVRKB2YWKXAEEY/state.json","well_known_bundle_url":"https://pith.science/.well-known/pith/T75T5AAJG5EOMVRKB2YWKXAEEY/bundle.json","status":"primary"}],"public_keys":[{"key_id":"pith-v1-2026-05","algorithm":"ed25519","format":"raw","public_key_b64":"stVStoiQhXFxp4s2pdzPNoqVNBMojDU/fJ2db5S3CbM=","public_key_hex":"b2d552b68890857171a78b36a5dccf368a953413288c353f7c9d9d6f94b709b3","fingerprint_sha256_b32_first128bits":"RVFV5Z2OI2J3ZUO7ERDEBCYNKS","fingerprint_sha256_hex":"8d4b5ee74e4693bcd1df2446408b0d54","rotates_at":null,"url":"https://pith.science/pith-signing-key.json","notes":"Pith uses this Ed25519 key to sign canonical record SHA-256 digests. Verify with: ed25519_verify(public_key, message=canonical_sha256_bytes, signature=base64decode(signature_b64))."}],"merge_version":"pith-open-graph-merge-v1","built_at":"2026-07-05T14:43:29Z","links":{"resolver":"https://pith.science/pith/T75T5AAJG5EOMVRKB2YWKXAEEY","bundle":"https://pith.science/pith/T75T5AAJG5EOMVRKB2YWKXAEEY/bundle.json","state":"https://pith.science/pith/T75T5AAJG5EOMVRKB2YWKXAEEY/state.json","well_known_bundle":"https://pith.science/.well-known/pith/T75T5AAJG5EOMVRKB2YWKXAEEY/bundle.json"},"state":{"state_type":"pith_open_graph_state","state_version":"1.0","pith_number":"pith:2022:T75T5AAJG5EOMVRKB2YWKXAEEY","merge_version":"pith-open-graph-merge-v1","event_count":2,"valid_event_count":2,"invalid_event_count":0,"equivocation_count":0,"current":{"canonical_record":{"metadata":{"abstract_canon_sha256":"e0a1db32e7de282e5c3ee16ff3b141f68c247bffb49b60c23a3439ee86666609","cross_cats_sorted":[],"license":"http://arxiv.org/licenses/nonexclusive-distrib/1.0/","primary_cat":"cs.LG","submitted_at":"2022-05-18T06:50:44Z","title_canon_sha256":"43d3fc41fd70d8006a88025c569aa903626a3175d0b30bc4aa7aa3f1f11b7e7e"},"schema_version":"1.0","source":{"id":"2205.08754","kind":"arxiv","version":1}},"source_aliases":[{"alias_kind":"arxiv","alias_value":"2205.08754","created_at":"2026-07-05T04:24:28Z"},{"alias_kind":"arxiv_version","alias_value":"2205.08754v1","created_at":"2026-07-05T04:24:28Z"},{"alias_kind":"doi","alias_value":"10.48550/arxiv.2205.08754","created_at":"2026-07-05T04:24:28Z"},{"alias_kind":"pith_short_12","alias_value":"T75T5AAJG5EO","created_at":"2026-07-05T04:24:28Z"},{"alias_kind":"pith_short_16","alias_value":"T75T5AAJG5EOMVRK","created_at":"2026-07-05T04:24:28Z"},{"alias_kind":"pith_short_8","alias_value":"T75T5AAJ","created_at":"2026-07-05T04:24:28Z"}],"graph_snapshots":[{"event_id":"sha256:684997299808e984919128e0760151e360fc11f97c081029ff877dbf445057fb","target":"graph","created_at":"2026-07-05T04:24:28Z","signer":{"key_id":"pith-v1-2026-05","public_key_fingerprint":"8d4b5ee74e4693bcd1df2446408b0d54","signer_id":"pith.science","signer_type":"pith_registry"},"payload":{"graph_snapshot":{"author_claims":{"count":0,"snapshot_sha256":"258153158e38e3291e3d48162225fcdb2d5a3ed65a07baac614ab91432fd4f57","strong_count":0},"builder_version":"pith-number-builder-2026-05-17-v1","claims":{"count":0,"items":[],"snapshot_sha256":"258153158e38e3291e3d48162225fcdb2d5a3ed65a07baac614ab91432fd4f57"},"formal_canon":{"evidence_count":0,"snapshot_sha256":"258153158e38e3291e3d48162225fcdb2d5a3ed65a07baac614ab91432fd4f57"},"integrity":{"available":true,"clean":true,"detectors_run":[],"endpoint":"/pith/2205.08754/integrity.json","findings":[],"snapshot_sha256":"c28c3603d3b5d939e8dc4c7e95fa8dfce3d595e45f758748cecf8e644a296938","summary":{"advisory":0,"by_detector":{},"critical":0,"informational":0}},"paper":{"abstract_excerpt":"Physics-informed neural networks (PINNs) provide a deep learning framework for numerically solving partial differential equations (PDEs), and have been widely used in a variety of PDE problems. However, there still remain some challenges in the application of PINNs: 1) the mechanism of PINNs is unsuitable (at least cannot be directly applied) to exploiting a small size of (usually very few) extra informative samples to refine the networks; and 2) the efficiency of training PINNs often becomes low for some complicated PDEs. In this paper, we propose the generative adversarial physics-informed n","authors_text":"Chao Zhang, Chuncheng Wang, Dacheng Tao, Hanting Guan, Wensheng Li","cross_cats":[],"headline":"","license":"http://arxiv.org/licenses/nonexclusive-distrib/1.0/","primary_cat":"cs.LG","submitted_at":"2022-05-18T06:50:44Z","title":"Revisiting PINNs: Generative Adversarial Physics-informed Neural Networks and Point-weighting Method"},"references":{"count":0,"internal_anchors":0,"resolved_work":0,"sample":[],"snapshot_sha256":"258153158e38e3291e3d48162225fcdb2d5a3ed65a07baac614ab91432fd4f57"},"source":{"id":"2205.08754","kind":"arxiv","version":1},"verdict":{"created_at":null,"id":null,"model_set":{},"one_line_summary":"","pipeline_version":null,"pith_extraction_headline":"","strongest_claim":"","weakest_assumption":""}},"verdict_id":null}}],"author_attestations":[],"timestamp_anchors":[],"storage_attestations":[],"citation_signatures":[],"replication_records":[],"corrections":[],"mirror_hints":[],"record_created":{"event_id":"sha256:d51d12dd910158ab5666ec83873a624c708b279d2a4a47c0e861b0611064a2e1","target":"record","created_at":"2026-07-05T04:24:28Z","signer":{"key_id":"pith-v1-2026-05","public_key_fingerprint":"8d4b5ee74e4693bcd1df2446408b0d54","signer_id":"pith.science","signer_type":"pith_registry"},"payload":{"attestation_state":"computed","canonical_record":{"metadata":{"abstract_canon_sha256":"e0a1db32e7de282e5c3ee16ff3b141f68c247bffb49b60c23a3439ee86666609","cross_cats_sorted":[],"license":"http://arxiv.org/licenses/nonexclusive-distrib/1.0/","primary_cat":"cs.LG","submitted_at":"2022-05-18T06:50:44Z","title_canon_sha256":"43d3fc41fd70d8006a88025c569aa903626a3175d0b30bc4aa7aa3f1f11b7e7e"},"schema_version":"1.0","source":{"id":"2205.08754","kind":"arxiv","version":1}},"canonical_sha256":"9ffb3e80093748e6562a0eb1655c042624df63fad1dbc58a46731bfe02cf04b9","receipt":{"algorithm":"ed25519","builder_version":"pith-number-builder-2026-05-17-v1","canonical_sha256":"9ffb3e80093748e6562a0eb1655c042624df63fad1dbc58a46731bfe02cf04b9","first_computed_at":"2026-07-05T04:24:28.803835Z","key_id":"pith-v1-2026-05","kind":"pith_receipt","last_reissued_at":"2026-07-05T04:24:28.803835Z","public_key_fingerprint":"8d4b5ee74e4693bcd1df2446408b0d54","receipt_version":"0.3","signature_b64":"icIn4qB8GK7lP569r3o091a+HiqFot5wbxfNLzjYlSPcuIYDEhShe73Q5o40CYLmCnLenWWYOpDSnHhT4eMICg==","signature_status":"signed_v1","signed_at":"2026-07-05T04:24:28.804277Z","signed_message":"canonical_sha256_bytes"},"source_id":"2205.08754","source_kind":"arxiv","source_version":1}}},"equivocations":[],"invalid_events":[],"applied_event_ids":["sha256:d51d12dd910158ab5666ec83873a624c708b279d2a4a47c0e861b0611064a2e1","sha256:684997299808e984919128e0760151e360fc11f97c081029ff877dbf445057fb"],"state_sha256":"3db01e74a44162e721ce9e3c7dea4d8b0f599e538b1416df0508d3a8984db221"},"bundle_signature":{"signature_status":"signed_v1","algorithm":"ed25519","key_id":"pith-v1-2026-05","public_key_fingerprint":"8d4b5ee74e4693bcd1df2446408b0d54","signature_b64":"+zWkuiXXGqlohpZORFGg6iz0Vn90dms7VMgb7E/b+HmGG9uSUztl49iJ6jQHOKsfIl6bIaMzzJR5ify8AD7uAA==","signed_message":"bundle_sha256_bytes","signed_at":"2026-07-05T14:43:29.344504Z","bundle_sha256":"a88a634857aff89112d70f278416d7fc8c9b0d98b84f9363c28102bc36b180d8"}}