{"bundle_type":"pith_open_graph_bundle","bundle_version":"1.0","pith_number":"pith:2025:KXZJVYL3ZRORBN6XFZBFLLWVT6","short_pith_number":"pith:KXZJVYL3","canonical_record":{"source":{"id":"2509.07245","kind":"arxiv","version":1},"metadata":{"license":"http://arxiv.org/licenses/nonexclusive-distrib/1.0/","primary_cat":"cs.LG","submitted_at":"2025-09-08T21:43:41Z","cross_cats_sorted":[],"title_canon_sha256":"42bac803878158d4f2259aa0bf2b315c015282c0c29c00100a598f087e1f55cb","abstract_canon_sha256":"07ef5a31e90c8ffb533143986aab20cc98109a30fcf9cbadc71ad442b618fdd3"},"schema_version":"1.0"},"canonical_sha256":"55f29ae17bcc5d10b7d72e4255aed59fa265d48db588d77e30eb70b5ee6897bd","source":{"kind":"arxiv","id":"2509.07245","version":1},"source_aliases":[{"alias_kind":"arxiv","alias_value":"2509.07245","created_at":"2026-07-05T12:07:18Z"},{"alias_kind":"arxiv_version","alias_value":"2509.07245v1","created_at":"2026-07-05T12:07:18Z"},{"alias_kind":"doi","alias_value":"10.48550/arxiv.2509.07245","created_at":"2026-07-05T12:07:18Z"},{"alias_kind":"pith_short_12","alias_value":"KXZJVYL3ZROR","created_at":"2026-07-05T12:07:18Z"},{"alias_kind":"pith_short_16","alias_value":"KXZJVYL3ZRORBN6X","created_at":"2026-07-05T12:07:18Z"},{"alias_kind":"pith_short_8","alias_value":"KXZJVYL3","created_at":"2026-07-05T12:07:18Z"}],"events":[{"event_type":"record_created","subject_pith_number":"pith:2025:KXZJVYL3ZRORBN6XFZBFLLWVT6","target":"record","payload":{"canonical_record":{"source":{"id":"2509.07245","kind":"arxiv","version":1},"metadata":{"license":"http://arxiv.org/licenses/nonexclusive-distrib/1.0/","primary_cat":"cs.LG","submitted_at":"2025-09-08T21:43:41Z","cross_cats_sorted":[],"title_canon_sha256":"42bac803878158d4f2259aa0bf2b315c015282c0c29c00100a598f087e1f55cb","abstract_canon_sha256":"07ef5a31e90c8ffb533143986aab20cc98109a30fcf9cbadc71ad442b618fdd3"},"schema_version":"1.0"},"canonical_sha256":"55f29ae17bcc5d10b7d72e4255aed59fa265d48db588d77e30eb70b5ee6897bd","receipt":{"kind":"pith_receipt","key_id":"pith-v1-2026-05","algorithm":"ed25519","signed_at":"2026-07-05T12:07:18.356421Z","signature_b64":"BaGhHxR2A1oxx9qP/IMWqcu622I/5wo0/kUfdaS1VUhQSWi8Ay1ewe3hy8nXWuzH6gDXeyTMvQp3Sfr93BxFAA==","signed_message":"canonical_sha256_bytes","builder_version":"pith-number-builder-2026-05-17-v1","receipt_version":"0.3","canonical_sha256":"55f29ae17bcc5d10b7d72e4255aed59fa265d48db588d77e30eb70b5ee6897bd","last_reissued_at":"2026-07-05T12:07:18.355902Z","signature_status":"signed_v1","first_computed_at":"2026-07-05T12:07:18.355902Z","public_key_fingerprint":"8d4b5ee74e4693bcd1df2446408b0d54"},"source_kind":"arxiv","source_id":"2509.07245","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":"2026-07-05T12:07:18Z","supersedes":[],"prev_event":null,"signature":{"signature_status":"signed_v1","algorithm":"ed25519","key_id":"pith-v1-2026-05","public_key_fingerprint":"8d4b5ee74e4693bcd1df2446408b0d54","signature_b64":"i2nA+xKIdWE9YP6w3ARhUE1GlHYLEajCOysfI98gN5aIWxLrHgEQOjdAACMt3ktl0JQE7M6TGmpepJBhLo6ADA==","signed_message":"open_graph_event_sha256_bytes","signed_at":"2026-08-07T09:45:46.153435Z"},"content_sha256":"557b56178d6136b78a6e1085989a7b3f45183d6bf8f582abb28781baa9d7f61b","schema_version":"1.0","event_id":"sha256:557b56178d6136b78a6e1085989a7b3f45183d6bf8f582abb28781baa9d7f61b"},{"event_type":"graph_snapshot","subject_pith_number":"pith:2025:KXZJVYL3ZRORBN6XFZBFLLWVT6","target":"graph","payload":{"graph_snapshot":{"paper":{"title":"IP-Basis PINNs: Efficient Multi-Query Inverse Parameter Estimation","license":"http://arxiv.org/licenses/nonexclusive-distrib/1.0/","headline":"","cross_cats":[],"primary_cat":"cs.LG","authors_text":"Mohammad Kohandel, Shalev Manor","submitted_at":"2025-09-08T21:43:41Z","abstract_excerpt":"Solving inverse problems with Physics-Informed Neural Networks (PINNs) is computationally expensive for multi-query scenarios, as each new set of observed data requires a new, expensive training procedure. We present Inverse-Parameter Basis PINNs (IP-Basis PINNs), a meta-learning framework that extends the foundational work of Desai et al. (2022) to enable rapid and efficient inference for inverse problems. Our method employs an offline-online decomposition: a deep network is first trained offline to produce a rich set of basis functions that span the solution space of a parametric differentia"},"claims":{"count":0,"items":[],"snapshot_sha256":"258153158e38e3291e3d48162225fcdb2d5a3ed65a07baac614ab91432fd4f57"},"source":{"id":"2509.07245","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/2509.07245/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":"2026-07-05T12:07:18Z","supersedes":[],"prev_event":null,"signature":{"signature_status":"signed_v1","algorithm":"ed25519","key_id":"pith-v1-2026-05","public_key_fingerprint":"8d4b5ee74e4693bcd1df2446408b0d54","signature_b64":"yHvtrDUPxnkUKyFVGvpgDEXtv7l7YHpZIkzASH+6nBF3VsFmAToazd1K2yGsMgdtWoM6vR8JJaCP+yhhnIGcAw==","signed_message":"open_graph_event_sha256_bytes","signed_at":"2026-08-07T09:45:46.154323Z"},"content_sha256":"aba0f03ed498a805d56776175ec1ec817e81b252267886fddc282c92a68f47f3","schema_version":"1.0","event_id":"sha256:aba0f03ed498a805d56776175ec1ec817e81b252267886fddc282c92a68f47f3"}],"timestamp_proofs":[],"mirror_hints":[{"mirror_type":"https","name":"Pith Resolver","base_url":"https://pith.science","bundle_url":"https://pith.science/pith/KXZJVYL3ZRORBN6XFZBFLLWVT6/bundle.json","state_url":"https://pith.science/pith/KXZJVYL3ZRORBN6XFZBFLLWVT6/state.json","well_known_bundle_url":"https://pith.science/.well-known/pith/KXZJVYL3ZRORBN6XFZBFLLWVT6/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":"2026-08-07T09:45:46Z","links":{"resolver":"https://pith.science/pith/KXZJVYL3ZRORBN6XFZBFLLWVT6","bundle":"https://pith.science/pith/KXZJVYL3ZRORBN6XFZBFLLWVT6/bundle.json","state":"https://pith.science/pith/KXZJVYL3ZRORBN6XFZBFLLWVT6/state.json","well_known_bundle":"https://pith.science/.well-known/pith/KXZJVYL3ZRORBN6XFZBFLLWVT6/bundle.json"},"state":{"state_type":"pith_open_graph_state","state_version":"1.0","pith_number":"pith:2025:KXZJVYL3ZRORBN6XFZBFLLWVT6","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":"07ef5a31e90c8ffb533143986aab20cc98109a30fcf9cbadc71ad442b618fdd3","cross_cats_sorted":[],"license":"http://arxiv.org/licenses/nonexclusive-distrib/1.0/","primary_cat":"cs.LG","submitted_at":"2025-09-08T21:43:41Z","title_canon_sha256":"42bac803878158d4f2259aa0bf2b315c015282c0c29c00100a598f087e1f55cb"},"schema_version":"1.0","source":{"id":"2509.07245","kind":"arxiv","version":1}},"source_aliases":[{"alias_kind":"arxiv","alias_value":"2509.07245","created_at":"2026-07-05T12:07:18Z"},{"alias_kind":"arxiv_version","alias_value":"2509.07245v1","created_at":"2026-07-05T12:07:18Z"},{"alias_kind":"doi","alias_value":"10.48550/arxiv.2509.07245","created_at":"2026-07-05T12:07:18Z"},{"alias_kind":"pith_short_12","alias_value":"KXZJVYL3ZROR","created_at":"2026-07-05T12:07:18Z"},{"alias_kind":"pith_short_16","alias_value":"KXZJVYL3ZRORBN6X","created_at":"2026-07-05T12:07:18Z"},{"alias_kind":"pith_short_8","alias_value":"KXZJVYL3","created_at":"2026-07-05T12:07:18Z"}],"graph_snapshots":[{"event_id":"sha256:aba0f03ed498a805d56776175ec1ec817e81b252267886fddc282c92a68f47f3","target":"graph","created_at":"2026-07-05T12:07:18Z","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/2509.07245/integrity.json","findings":[],"snapshot_sha256":"c28c3603d3b5d939e8dc4c7e95fa8dfce3d595e45f758748cecf8e644a296938","summary":{"advisory":0,"by_detector":{},"critical":0,"informational":0}},"paper":{"abstract_excerpt":"Solving inverse problems with Physics-Informed Neural Networks (PINNs) is computationally expensive for multi-query scenarios, as each new set of observed data requires a new, expensive training procedure. We present Inverse-Parameter Basis PINNs (IP-Basis PINNs), a meta-learning framework that extends the foundational work of Desai et al. (2022) to enable rapid and efficient inference for inverse problems. Our method employs an offline-online decomposition: a deep network is first trained offline to produce a rich set of basis functions that span the solution space of a parametric differentia","authors_text":"Mohammad Kohandel, Shalev Manor","cross_cats":[],"headline":"","license":"http://arxiv.org/licenses/nonexclusive-distrib/1.0/","primary_cat":"cs.LG","submitted_at":"2025-09-08T21:43:41Z","title":"IP-Basis PINNs: Efficient Multi-Query Inverse Parameter Estimation"},"references":{"count":0,"internal_anchors":0,"resolved_work":0,"sample":[],"snapshot_sha256":"258153158e38e3291e3d48162225fcdb2d5a3ed65a07baac614ab91432fd4f57"},"source":{"id":"2509.07245","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":"sha256:557b56178d6136b78a6e1085989a7b3f45183d6bf8f582abb28781baa9d7f61b","target":"record","created_at":"2026-07-05T12:07:18Z","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":"07ef5a31e90c8ffb533143986aab20cc98109a30fcf9cbadc71ad442b618fdd3","cross_cats_sorted":[],"license":"http://arxiv.org/licenses/nonexclusive-distrib/1.0/","primary_cat":"cs.LG","submitted_at":"2025-09-08T21:43:41Z","title_canon_sha256":"42bac803878158d4f2259aa0bf2b315c015282c0c29c00100a598f087e1f55cb"},"schema_version":"1.0","source":{"id":"2509.07245","kind":"arxiv","version":1}},"canonical_sha256":"55f29ae17bcc5d10b7d72e4255aed59fa265d48db588d77e30eb70b5ee6897bd","receipt":{"algorithm":"ed25519","builder_version":"pith-number-builder-2026-05-17-v1","canonical_sha256":"55f29ae17bcc5d10b7d72e4255aed59fa265d48db588d77e30eb70b5ee6897bd","first_computed_at":"2026-07-05T12:07:18.355902Z","key_id":"pith-v1-2026-05","kind":"pith_receipt","last_reissued_at":"2026-07-05T12:07:18.355902Z","public_key_fingerprint":"8d4b5ee74e4693bcd1df2446408b0d54","receipt_version":"0.3","signature_b64":"BaGhHxR2A1oxx9qP/IMWqcu622I/5wo0/kUfdaS1VUhQSWi8Ay1ewe3hy8nXWuzH6gDXeyTMvQp3Sfr93BxFAA==","signature_status":"signed_v1","signed_at":"2026-07-05T12:07:18.356421Z","signed_message":"canonical_sha256_bytes"},"source_id":"2509.07245","source_kind":"arxiv","source_version":1}}},"equivocations":[],"invalid_events":[],"applied_event_ids":["sha256:557b56178d6136b78a6e1085989a7b3f45183d6bf8f582abb28781baa9d7f61b","sha256:aba0f03ed498a805d56776175ec1ec817e81b252267886fddc282c92a68f47f3"],"state_sha256":"169bc75296c366cd67cc061eb8eadd1bf894ee680bb3de9131f6573f7b25fb64"},"bundle_signature":{"signature_status":"signed_v1","algorithm":"ed25519","key_id":"pith-v1-2026-05","public_key_fingerprint":"8d4b5ee74e4693bcd1df2446408b0d54","signature_b64":"+VfSba+OoAgZQ5ypmwGIONge4ELFSYRzX2L0OXSH5oeplhwJmw33D5ZjbjplQu0Gdrpj5YHTRHX/bRQoDTpmDA==","signed_message":"bundle_sha256_bytes","signed_at":"2026-08-07T09:45:46.160596Z","bundle_sha256":"3ae0da9be28790a6343acefb391590b46ddd8f0334c6d9f1b22788339242c1ac"}}