{"record_type":"pith_number_record","schema_url":"https://pith.science/schemas/pith-number/v1.json","pith_number":"pith:2026:OQ3KAGMY5WZ47G376CGRRQVBGX","short_pith_number":"pith:OQ3KAGMY","schema_version":"1.0","canonical_sha256":"7436a01998edb3cf9b7ff08d18c2a135fda75b968e594ed4aa91825243de484c","source":{"kind":"arxiv","id":"2606.17460","version":1},"attestation_state":"computed","paper":{"title":"Operator Boosting Produces Pareto-Efficient PDE Surrogates","license":"http://arxiv.org/licenses/nonexclusive-distrib/1.0/","headline":"","cross_cats":["cs.NA","math.NA","physics.comp-ph"],"primary_cat":"cs.LG","authors_text":"Lennon J. Shikhman","submitted_at":"2026-06-16T03:20:44Z","abstract_excerpt":"Neural operators are widely used as surrogate solution maps for partial differential equations (PDEs), but full-size models can be costly to store, deploy, and evaluate in many-query scientific workflows. This work introduces Operator Boosting, a stagewise residual-learning framework for constructing compact neural-operator surrogates directly, rather than training a large model and compressing it afterward. Starting from the empirical mean predictor in normalized output coordinates, the method trains a sequence of tiny same-family neural operators on residual fields and incorporates each corr"},"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":"2606.17460","kind":"arxiv","version":1},"metadata":{"license":"http://arxiv.org/licenses/nonexclusive-distrib/1.0/","primary_cat":"cs.LG","submitted_at":"2026-06-16T03:20:44Z","cross_cats_sorted":["cs.NA","math.NA","physics.comp-ph"],"title_canon_sha256":"4159e8dc0c5170e1e2cc6c2ba0f343cb113286742aa64392e8d404b82996902c","abstract_canon_sha256":"75dce079ac3bc6abe1ddc15093587335070ac318a4992d0b549dfd718c75b52a"},"schema_version":"1.0"},"receipt":{"kind":"pith_receipt","key_id":"pith-v1-2026-05","algorithm":"ed25519","signed_at":"2026-06-19T16:10:13.564709Z","signature_b64":"sKZweN8kXiiLWYcI6h3o3UN1EI43UI3SOx3WJc0aXyTctpUzJ970N3b0S5JcQfgZoXq7RtIeH0K/2FqI3Z90Bw==","signed_message":"canonical_sha256_bytes","builder_version":"pith-number-builder-2026-05-17-v1","receipt_version":"0.3","canonical_sha256":"7436a01998edb3cf9b7ff08d18c2a135fda75b968e594ed4aa91825243de484c","last_reissued_at":"2026-06-19T16:10:13.564337Z","signature_status":"signed_v1","first_computed_at":"2026-06-19T16:10:13.564337Z","public_key_fingerprint":"8d4b5ee74e4693bcd1df2446408b0d54"},"graph_snapshot":{"paper":{"title":"Operator Boosting Produces Pareto-Efficient PDE Surrogates","license":"http://arxiv.org/licenses/nonexclusive-distrib/1.0/","headline":"","cross_cats":["cs.NA","math.NA","physics.comp-ph"],"primary_cat":"cs.LG","authors_text":"Lennon J. Shikhman","submitted_at":"2026-06-16T03:20:44Z","abstract_excerpt":"Neural operators are widely used as surrogate solution maps for partial differential equations (PDEs), but full-size models can be costly to store, deploy, and evaluate in many-query scientific workflows. This work introduces Operator Boosting, a stagewise residual-learning framework for constructing compact neural-operator surrogates directly, rather than training a large model and compressing it afterward. Starting from the empirical mean predictor in normalized output coordinates, the method trains a sequence of tiny same-family neural operators on residual fields and incorporates each corr"},"claims":{"count":0,"items":[],"snapshot_sha256":"258153158e38e3291e3d48162225fcdb2d5a3ed65a07baac614ab91432fd4f57"},"source":{"id":"2606.17460","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/2606.17460/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":"2606.17460","created_at":"2026-06-19T16:10:13.564399+00:00"},{"alias_kind":"arxiv_version","alias_value":"2606.17460v1","created_at":"2026-06-19T16:10:13.564399+00:00"},{"alias_kind":"doi","alias_value":"10.48550/arxiv.2606.17460","created_at":"2026-06-19T16:10:13.564399+00:00"},{"alias_kind":"pith_short_12","alias_value":"OQ3KAGMY5WZ4","created_at":"2026-06-19T16:10:13.564399+00:00"},{"alias_kind":"pith_short_16","alias_value":"OQ3KAGMY5WZ47G37","created_at":"2026-06-19T16:10:13.564399+00:00"},{"alias_kind":"pith_short_8","alias_value":"OQ3KAGMY","created_at":"2026-06-19T16:10:13.564399+00:00"}],"events":[],"event_summary":{},"paper_claims":[],"inbound_citations":{"count":0,"internal_anchor_count":0,"sample":[]},"formal_canon":{"evidence_count":0,"sample":[],"anchors":[]},"links":{"html":"https://pith.science/pith/OQ3KAGMY5WZ47G376CGRRQVBGX","json":"https://pith.science/pith/OQ3KAGMY5WZ47G376CGRRQVBGX.json","graph_json":"https://pith.science/api/pith-number/OQ3KAGMY5WZ47G376CGRRQVBGX/graph.json","events_json":"https://pith.science/api/pith-number/OQ3KAGMY5WZ47G376CGRRQVBGX/events.json","paper":"https://pith.science/paper/OQ3KAGMY"},"agent_actions":{"view_html":"https://pith.science/pith/OQ3KAGMY5WZ47G376CGRRQVBGX","download_json":"https://pith.science/pith/OQ3KAGMY5WZ47G376CGRRQVBGX.json","view_paper":"https://pith.science/paper/OQ3KAGMY","resolve_alias":"https://pith.science/api/pith-number/resolve?arxiv=2606.17460&json=true","fetch_graph":"https://pith.science/api/pith-number/OQ3KAGMY5WZ47G376CGRRQVBGX/graph.json","fetch_events":"https://pith.science/api/pith-number/OQ3KAGMY5WZ47G376CGRRQVBGX/events.json","actions":{"anchor_timestamp":"https://pith.science/pith/OQ3KAGMY5WZ47G376CGRRQVBGX/action/timestamp_anchor","attest_storage":"https://pith.science/pith/OQ3KAGMY5WZ47G376CGRRQVBGX/action/storage_attestation","attest_author":"https://pith.science/pith/OQ3KAGMY5WZ47G376CGRRQVBGX/action/author_attestation","sign_citation":"https://pith.science/pith/OQ3KAGMY5WZ47G376CGRRQVBGX/action/citation_signature","submit_replication":"https://pith.science/pith/OQ3KAGMY5WZ47G376CGRRQVBGX/action/replication_record"}},"created_at":"2026-06-19T16:10:13.564399+00:00","updated_at":"2026-06-19T16:10:13.564399+00:00"}