{"bundle_type":"pith_open_graph_bundle","bundle_version":"1.0","pith_number":"pith:2022:GLGNDRWDQZ2T4Y3YTKU73BATZ4","short_pith_number":"pith:GLGNDRWD","canonical_record":{"source":{"id":"2211.01604","kind":"arxiv","version":1},"metadata":{"license":"http://arxiv.org/licenses/nonexclusive-distrib/1.0/","primary_cat":"cs.LG","submitted_at":"2022-11-03T06:17:52Z","cross_cats_sorted":["cs.AI"],"title_canon_sha256":"3294001a6c338b9df465839b4b703f179f71f9de4906d7ab79fd169f8630e456","abstract_canon_sha256":"4f47f47a3e7bd924e3cb832f02a7499d1288f3b385a4701deff9e668b714bd90"},"schema_version":"1.0"},"canonical_sha256":"32ccd1c6c386753e63789aa9fd8413cf0dfca1c0c20d4e7c7ad23abc0c9f7d51","source":{"kind":"arxiv","id":"2211.01604","version":1},"source_aliases":[{"alias_kind":"arxiv","alias_value":"2211.01604","created_at":"2026-07-05T05:12:57Z"},{"alias_kind":"arxiv_version","alias_value":"2211.01604v1","created_at":"2026-07-05T05:12:57Z"},{"alias_kind":"doi","alias_value":"10.48550/arxiv.2211.01604","created_at":"2026-07-05T05:12:57Z"},{"alias_kind":"pith_short_12","alias_value":"GLGNDRWDQZ2T","created_at":"2026-07-05T05:12:57Z"},{"alias_kind":"pith_short_16","alias_value":"GLGNDRWDQZ2T4Y3Y","created_at":"2026-07-05T05:12:57Z"},{"alias_kind":"pith_short_8","alias_value":"GLGNDRWD","created_at":"2026-07-05T05:12:57Z"}],"events":[{"event_type":"record_created","subject_pith_number":"pith:2022:GLGNDRWDQZ2T4Y3YTKU73BATZ4","target":"record","payload":{"canonical_record":{"source":{"id":"2211.01604","kind":"arxiv","version":1},"metadata":{"license":"http://arxiv.org/licenses/nonexclusive-distrib/1.0/","primary_cat":"cs.LG","submitted_at":"2022-11-03T06:17:52Z","cross_cats_sorted":["cs.AI"],"title_canon_sha256":"3294001a6c338b9df465839b4b703f179f71f9de4906d7ab79fd169f8630e456","abstract_canon_sha256":"4f47f47a3e7bd924e3cb832f02a7499d1288f3b385a4701deff9e668b714bd90"},"schema_version":"1.0"},"canonical_sha256":"32ccd1c6c386753e63789aa9fd8413cf0dfca1c0c20d4e7c7ad23abc0c9f7d51","receipt":{"kind":"pith_receipt","key_id":"pith-v1-2026-05","algorithm":"ed25519","signed_at":"2026-07-05T05:12:57.977280Z","signature_b64":"XnP7ivVD4q8AyqdiOuAVg8Y2e2hoRDKj6oTCf6H9pjQb+lyu2LuJnvt/5GPjoVnb//kf9AKiivfV9SMkQGXbBA==","signed_message":"canonical_sha256_bytes","builder_version":"pith-number-builder-2026-05-17-v1","receipt_version":"0.3","canonical_sha256":"32ccd1c6c386753e63789aa9fd8413cf0dfca1c0c20d4e7c7ad23abc0c9f7d51","last_reissued_at":"2026-07-05T05:12:57.976869Z","signature_status":"signed_v1","first_computed_at":"2026-07-05T05:12:57.976869Z","public_key_fingerprint":"8d4b5ee74e4693bcd1df2446408b0d54"},"source_kind":"arxiv","source_id":"2211.01604","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":"2026-07-05T05:12:57Z","supersedes":[],"prev_event":null,"signature":{"signature_status":"signed_v1","algorithm":"ed25519","key_id":"pith-v1-2026-05","public_key_fingerprint":"8d4b5ee74e4693bcd1df2446408b0d54","signature_b64":"MRldgJjjnGNrVRON0jqPkyWklCTeetlGavPHPqdnjwwDbDCaoDcvoeiyfEoTDg64o1R33PCNXVf7gOVHGlTqCg==","signed_message":"open_graph_event_sha256_bytes","signed_at":"2026-08-05T13:19:21.213579Z"},"content_sha256":"5c17d2af744d1c4111d31ea053f175e562bbc87c5941237846b5921b81c7cfd6","schema_version":"1.0","event_id":"sha256:5c17d2af744d1c4111d31ea053f175e562bbc87c5941237846b5921b81c7cfd6"},{"event_type":"graph_snapshot","subject_pith_number":"pith:2022:GLGNDRWDQZ2T4Y3YTKU73BATZ4","target":"graph","payload":{"graph_snapshot":{"paper":{"title":"Meta-PDE: Learning to Solve PDEs Quickly Without a Mesh","license":"http://arxiv.org/licenses/nonexclusive-distrib/1.0/","headline":"","cross_cats":["cs.AI"],"primary_cat":"cs.LG","authors_text":"Alex Beatson, Deniz Oktay, Nick McGreivy, Ryan P. Adams, Tian Qin","submitted_at":"2022-11-03T06:17:52Z","abstract_excerpt":"Partial differential equations (PDEs) are often computationally challenging to solve, and in many settings many related PDEs must be be solved either at every timestep or for a variety of candidate boundary conditions, parameters, or geometric domains. We present a meta-learning based method which learns to rapidly solve problems from a distribution of related PDEs. We use meta-learning (MAML and LEAP) to identify initializations for a neural network representation of the PDE solution such that a residual of the PDE can be quickly minimized on a novel task. We apply our meta-solving approach t"},"claims":{"count":0,"items":[],"snapshot_sha256":"258153158e38e3291e3d48162225fcdb2d5a3ed65a07baac614ab91432fd4f57"},"source":{"id":"2211.01604","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/2211.01604/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":"2026-07-05T05:12:57Z","supersedes":[],"prev_event":null,"signature":{"signature_status":"signed_v1","algorithm":"ed25519","key_id":"pith-v1-2026-05","public_key_fingerprint":"8d4b5ee74e4693bcd1df2446408b0d54","signature_b64":"q+UXOtpDmMRI4UzIPpWJ4jjjgyGqOAFG4fZYDfsjUbayLSwKA2YBdTfJkZNUwzQZjkMx1ntDUtygdwhm4VihBA==","signed_message":"open_graph_event_sha256_bytes","signed_at":"2026-08-05T13:19:21.214084Z"},"content_sha256":"45f8db472abd8fa14f8a9250bef634fa9d133e9a0219ce8ac1d13083e35da427","schema_version":"1.0","event_id":"sha256:45f8db472abd8fa14f8a9250bef634fa9d133e9a0219ce8ac1d13083e35da427"}],"timestamp_proofs":[],"mirror_hints":[{"mirror_type":"https","name":"Pith Resolver","base_url":"https://pith.science","bundle_url":"https://pith.science/pith/GLGNDRWDQZ2T4Y3YTKU73BATZ4/bundle.json","state_url":"https://pith.science/pith/GLGNDRWDQZ2T4Y3YTKU73BATZ4/state.json","well_known_bundle_url":"https://pith.science/.well-known/pith/GLGNDRWDQZ2T4Y3YTKU73BATZ4/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":"2026-08-05T13:19:21Z","links":{"resolver":"https://pith.science/pith/GLGNDRWDQZ2T4Y3YTKU73BATZ4","bundle":"https://pith.science/pith/GLGNDRWDQZ2T4Y3YTKU73BATZ4/bundle.json","state":"https://pith.science/pith/GLGNDRWDQZ2T4Y3YTKU73BATZ4/state.json","well_known_bundle":"https://pith.science/.well-known/pith/GLGNDRWDQZ2T4Y3YTKU73BATZ4/bundle.json"},"state":{"state_type":"pith_open_graph_state","state_version":"1.0","pith_number":"pith:2022:GLGNDRWDQZ2T4Y3YTKU73BATZ4","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":"4f47f47a3e7bd924e3cb832f02a7499d1288f3b385a4701deff9e668b714bd90","cross_cats_sorted":["cs.AI"],"license":"http://arxiv.org/licenses/nonexclusive-distrib/1.0/","primary_cat":"cs.LG","submitted_at":"2022-11-03T06:17:52Z","title_canon_sha256":"3294001a6c338b9df465839b4b703f179f71f9de4906d7ab79fd169f8630e456"},"schema_version":"1.0","source":{"id":"2211.01604","kind":"arxiv","version":1}},"source_aliases":[{"alias_kind":"arxiv","alias_value":"2211.01604","created_at":"2026-07-05T05:12:57Z"},{"alias_kind":"arxiv_version","alias_value":"2211.01604v1","created_at":"2026-07-05T05:12:57Z"},{"alias_kind":"doi","alias_value":"10.48550/arxiv.2211.01604","created_at":"2026-07-05T05:12:57Z"},{"alias_kind":"pith_short_12","alias_value":"GLGNDRWDQZ2T","created_at":"2026-07-05T05:12:57Z"},{"alias_kind":"pith_short_16","alias_value":"GLGNDRWDQZ2T4Y3Y","created_at":"2026-07-05T05:12:57Z"},{"alias_kind":"pith_short_8","alias_value":"GLGNDRWD","created_at":"2026-07-05T05:12:57Z"}],"graph_snapshots":[{"event_id":"sha256:45f8db472abd8fa14f8a9250bef634fa9d133e9a0219ce8ac1d13083e35da427","target":"graph","created_at":"2026-07-05T05:12:57Z","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/2211.01604/integrity.json","findings":[],"snapshot_sha256":"c28c3603d3b5d939e8dc4c7e95fa8dfce3d595e45f758748cecf8e644a296938","summary":{"advisory":0,"by_detector":{},"critical":0,"informational":0}},"paper":{"abstract_excerpt":"Partial differential equations (PDEs) are often computationally challenging to solve, and in many settings many related PDEs must be be solved either at every timestep or for a variety of candidate boundary conditions, parameters, or geometric domains. We present a meta-learning based method which learns to rapidly solve problems from a distribution of related PDEs. We use meta-learning (MAML and LEAP) to identify initializations for a neural network representation of the PDE solution such that a residual of the PDE can be quickly minimized on a novel task. We apply our meta-solving approach t","authors_text":"Alex Beatson, Deniz Oktay, Nick McGreivy, Ryan P. Adams, Tian Qin","cross_cats":["cs.AI"],"headline":"","license":"http://arxiv.org/licenses/nonexclusive-distrib/1.0/","primary_cat":"cs.LG","submitted_at":"2022-11-03T06:17:52Z","title":"Meta-PDE: Learning to Solve PDEs Quickly Without a Mesh"},"references":{"count":0,"internal_anchors":0,"resolved_work":0,"sample":[],"snapshot_sha256":"258153158e38e3291e3d48162225fcdb2d5a3ed65a07baac614ab91432fd4f57"},"source":{"id":"2211.01604","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":"sha256:5c17d2af744d1c4111d31ea053f175e562bbc87c5941237846b5921b81c7cfd6","target":"record","created_at":"2026-07-05T05:12:57Z","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":"4f47f47a3e7bd924e3cb832f02a7499d1288f3b385a4701deff9e668b714bd90","cross_cats_sorted":["cs.AI"],"license":"http://arxiv.org/licenses/nonexclusive-distrib/1.0/","primary_cat":"cs.LG","submitted_at":"2022-11-03T06:17:52Z","title_canon_sha256":"3294001a6c338b9df465839b4b703f179f71f9de4906d7ab79fd169f8630e456"},"schema_version":"1.0","source":{"id":"2211.01604","kind":"arxiv","version":1}},"canonical_sha256":"32ccd1c6c386753e63789aa9fd8413cf0dfca1c0c20d4e7c7ad23abc0c9f7d51","receipt":{"algorithm":"ed25519","builder_version":"pith-number-builder-2026-05-17-v1","canonical_sha256":"32ccd1c6c386753e63789aa9fd8413cf0dfca1c0c20d4e7c7ad23abc0c9f7d51","first_computed_at":"2026-07-05T05:12:57.976869Z","key_id":"pith-v1-2026-05","kind":"pith_receipt","last_reissued_at":"2026-07-05T05:12:57.976869Z","public_key_fingerprint":"8d4b5ee74e4693bcd1df2446408b0d54","receipt_version":"0.3","signature_b64":"XnP7ivVD4q8AyqdiOuAVg8Y2e2hoRDKj6oTCf6H9pjQb+lyu2LuJnvt/5GPjoVnb//kf9AKiivfV9SMkQGXbBA==","signature_status":"signed_v1","signed_at":"2026-07-05T05:12:57.977280Z","signed_message":"canonical_sha256_bytes"},"source_id":"2211.01604","source_kind":"arxiv","source_version":1}}},"equivocations":[],"invalid_events":[],"applied_event_ids":["sha256:5c17d2af744d1c4111d31ea053f175e562bbc87c5941237846b5921b81c7cfd6","sha256:45f8db472abd8fa14f8a9250bef634fa9d133e9a0219ce8ac1d13083e35da427"],"state_sha256":"e562af0dc95a77e750706875eedc0dd4c70aaf9037f33fe2827f6c14e7db3917"},"bundle_signature":{"signature_status":"signed_v1","algorithm":"ed25519","key_id":"pith-v1-2026-05","public_key_fingerprint":"8d4b5ee74e4693bcd1df2446408b0d54","signature_b64":"aTaEqpwkVgMFpcZ2eFB8uA2knnTah1+3gkExDbYrQuroprXUNT9+8CXKJbwcWCB/9nOHwA+EuZ0bk34fMHS4Bg==","signed_message":"bundle_sha256_bytes","signed_at":"2026-08-05T13:19:21.229589Z","bundle_sha256":"abce0a237d99f38e05e8334cb60391bcda033736c48bd6a114ff4ce48ab2cc74"}}