{"state_type":"pith_open_graph_state","state_version":"1.0","pith_number":"pith:2024:DX55UM3Y454RPLHBDYWLTQIOI3","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":"a8d460e3dc25d2d62b53cd99015aefbfd2b3e86fc51940b692896a537f25e7fb","cross_cats_sorted":["cs.AI"],"license":"http://arxiv.org/licenses/nonexclusive-distrib/1.0/","primary_cat":"cs.LG","submitted_at":"2024-10-31T12:51:40Z","title_canon_sha256":"d5e31a2f64b8b56ca90792e4fd87f31a226403e4c30edf1bbc673364ee995837"},"schema_version":"1.0","source":{"id":"2410.23889","kind":"arxiv","version":2}},"source_aliases":[{"alias_kind":"arxiv","alias_value":"2410.23889","created_at":"2026-07-05T09:32:49Z"},{"alias_kind":"arxiv_version","alias_value":"2410.23889v2","created_at":"2026-07-05T09:32:49Z"},{"alias_kind":"doi","alias_value":"10.48550/arxiv.2410.23889","created_at":"2026-07-05T09:32:49Z"},{"alias_kind":"pith_short_12","alias_value":"DX55UM3Y454R","created_at":"2026-07-05T09:32:49Z"},{"alias_kind":"pith_short_16","alias_value":"DX55UM3Y454RPLHB","created_at":"2026-07-05T09:32:49Z"},{"alias_kind":"pith_short_8","alias_value":"DX55UM3Y","created_at":"2026-07-05T09:32:49Z"}],"graph_snapshots":[{"event_id":"sha256:f2001f0b6bacb88db9f8b1276d4c96fb263529a8bdb87f7cc4d0d255b53e420b","target":"graph","created_at":"2026-07-05T09:32:49Z","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/2410.23889/integrity.json","findings":[],"snapshot_sha256":"c28c3603d3b5d939e8dc4c7e95fa8dfce3d595e45f758748cecf8e644a296938","summary":{"advisory":0,"by_detector":{},"critical":0,"informational":0}},"paper":{"abstract_excerpt":"Solving parametric partial differential equations (PDEs) presents significant challenges for data-driven methods due to the sensitivity of spatio-temporal dynamics to variations in PDE parameters. Machine learning approaches often struggle to capture this variability. To address this, data-driven approaches learn parametric PDEs by sampling a very large variety of trajectories with varying PDE parameters. We first show that incorporating conditioning mechanisms for learning parametric PDEs is essential and that among them, $\\textit{adaptive conditioning}$, allows stronger generalization. As ex","authors_text":"Armand Kassa\\\"i Koupa\\\"i, Jean-No\\\"el Vittaut, Jorge Mifsut Benet, Patrick Gallinari, Yuan Yin","cross_cats":["cs.AI"],"headline":"","license":"http://arxiv.org/licenses/nonexclusive-distrib/1.0/","primary_cat":"cs.LG","submitted_at":"2024-10-31T12:51:40Z","title":"GEPS: Boosting Generalization in Parametric PDE Neural Solvers through Adaptive Conditioning"},"references":{"count":0,"internal_anchors":0,"resolved_work":0,"sample":[],"snapshot_sha256":"258153158e38e3291e3d48162225fcdb2d5a3ed65a07baac614ab91432fd4f57"},"source":{"id":"2410.23889","kind":"arxiv","version":2},"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:c965867f00b3832874e32d0f8d8c3f672cf1fd1cd9c8fb270f5dc9193c1d769b","target":"record","created_at":"2026-07-05T09:32:49Z","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":"a8d460e3dc25d2d62b53cd99015aefbfd2b3e86fc51940b692896a537f25e7fb","cross_cats_sorted":["cs.AI"],"license":"http://arxiv.org/licenses/nonexclusive-distrib/1.0/","primary_cat":"cs.LG","submitted_at":"2024-10-31T12:51:40Z","title_canon_sha256":"d5e31a2f64b8b56ca90792e4fd87f31a226403e4c30edf1bbc673364ee995837"},"schema_version":"1.0","source":{"id":"2410.23889","kind":"arxiv","version":2}},"canonical_sha256":"1dfbda3378e77917ace11e2cb9c10e46e82ed045b0b942f5175ab25134824ea3","receipt":{"algorithm":"ed25519","builder_version":"pith-number-builder-2026-05-17-v1","canonical_sha256":"1dfbda3378e77917ace11e2cb9c10e46e82ed045b0b942f5175ab25134824ea3","first_computed_at":"2026-07-05T09:32:49.387268Z","key_id":"pith-v1-2026-05","kind":"pith_receipt","last_reissued_at":"2026-07-05T09:32:49.387268Z","public_key_fingerprint":"8d4b5ee74e4693bcd1df2446408b0d54","receipt_version":"0.3","signature_b64":"jyd0B4x6+c8GzhYdcLSPbzBPRSmKhMmZ3Oz/RQ/v2Z85JiAcuQ/wG8BzlUpqrCN7rms/Urmoh3J/q3cC/pu/Bw==","signature_status":"signed_v1","signed_at":"2026-07-05T09:32:49.387787Z","signed_message":"canonical_sha256_bytes"},"source_id":"2410.23889","source_kind":"arxiv","source_version":2}}},"equivocations":[],"invalid_events":[],"applied_event_ids":["sha256:c965867f00b3832874e32d0f8d8c3f672cf1fd1cd9c8fb270f5dc9193c1d769b","sha256:f2001f0b6bacb88db9f8b1276d4c96fb263529a8bdb87f7cc4d0d255b53e420b"],"state_sha256":"69ce6fe341eeac27df8d0660c6125d369ace6adb8a5252b14d894c102e8522f2"}