{"record_type":"pith_number_record","schema_url":"https://pith.science/schemas/pith-number/v1.json","pith_number":"pith:2025:HLL5LGE77ERVUTJ4RITNA4KDUL","short_pith_number":"pith:HLL5LGE7","schema_version":"1.0","canonical_sha256":"3ad7d5989ff9235a4d3c8a26d07143a2e99cafef93292351a3687189c1f9102a","source":{"kind":"arxiv","id":"2509.05732","version":1},"attestation_state":"computed","paper":{"title":"Simulation Priors for Data-Efficient Deep Learning","license":"http://arxiv.org/licenses/nonexclusive-distrib/1.0/","headline":"","cross_cats":["cs.AI"],"primary_cat":"cs.LG","authors_text":"Andreas Krause, Bhavya Sukhija, Florian D\\\"orfler, Jonas Rothfuss, Lenart Treven, Stelian Coros","submitted_at":"2025-09-06T14:36:41Z","abstract_excerpt":"How do we enable AI systems to efficiently learn in the real-world? First-principles models are widely used to simulate natural systems, but often fail to capture real-world complexity due to simplifying assumptions. In contrast, deep learning approaches can estimate complex dynamics with minimal assumptions but require large, representative datasets. We propose SimPEL, a method that efficiently combines first-principles models with data-driven learning by using low-fidelity simulators as priors in Bayesian deep learning. This enables SimPEL to benefit from simulator knowledge in low-data regi"},"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":"2509.05732","kind":"arxiv","version":1},"metadata":{"license":"http://arxiv.org/licenses/nonexclusive-distrib/1.0/","primary_cat":"cs.LG","submitted_at":"2025-09-06T14:36:41Z","cross_cats_sorted":["cs.AI"],"title_canon_sha256":"b792e7aafe6221139fce9806d9a85cf8150b60916635e0c77fa396eed24f6be1","abstract_canon_sha256":"4b87013982b55f39534244ae8a66e7b37200038829ea35ea8067a0d82971fb35"},"schema_version":"1.0"},"receipt":{"kind":"pith_receipt","key_id":"pith-v1-2026-05","algorithm":"ed25519","signed_at":"2026-07-05T12:06:23.049549Z","signature_b64":"FYPTTo9wY67gGxiLzUIP6n5buDsJrR5ahC7tcwmESjjeXczrJ0+yyXZHoi5JC6n+tVapzdxbmCCFlFaK42e5DA==","signed_message":"canonical_sha256_bytes","builder_version":"pith-number-builder-2026-05-17-v1","receipt_version":"0.3","canonical_sha256":"3ad7d5989ff9235a4d3c8a26d07143a2e99cafef93292351a3687189c1f9102a","last_reissued_at":"2026-07-05T12:06:23.049083Z","signature_status":"signed_v1","first_computed_at":"2026-07-05T12:06:23.049083Z","public_key_fingerprint":"8d4b5ee74e4693bcd1df2446408b0d54"},"graph_snapshot":{"paper":{"title":"Simulation Priors for Data-Efficient Deep Learning","license":"http://arxiv.org/licenses/nonexclusive-distrib/1.0/","headline":"","cross_cats":["cs.AI"],"primary_cat":"cs.LG","authors_text":"Andreas Krause, Bhavya Sukhija, Florian D\\\"orfler, Jonas Rothfuss, Lenart Treven, Stelian Coros","submitted_at":"2025-09-06T14:36:41Z","abstract_excerpt":"How do we enable AI systems to efficiently learn in the real-world? First-principles models are widely used to simulate natural systems, but often fail to capture real-world complexity due to simplifying assumptions. In contrast, deep learning approaches can estimate complex dynamics with minimal assumptions but require large, representative datasets. We propose SimPEL, a method that efficiently combines first-principles models with data-driven learning by using low-fidelity simulators as priors in Bayesian deep learning. This enables SimPEL to benefit from simulator knowledge in low-data regi"},"claims":{"count":0,"items":[],"snapshot_sha256":"258153158e38e3291e3d48162225fcdb2d5a3ed65a07baac614ab91432fd4f57"},"source":{"id":"2509.05732","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/2509.05732/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":"2509.05732","created_at":"2026-07-05T12:06:23.049137+00:00"},{"alias_kind":"arxiv_version","alias_value":"2509.05732v1","created_at":"2026-07-05T12:06:23.049137+00:00"},{"alias_kind":"doi","alias_value":"10.48550/arxiv.2509.05732","created_at":"2026-07-05T12:06:23.049137+00:00"},{"alias_kind":"pith_short_12","alias_value":"HLL5LGE77ERV","created_at":"2026-07-05T12:06:23.049137+00:00"},{"alias_kind":"pith_short_16","alias_value":"HLL5LGE77ERVUTJ4","created_at":"2026-07-05T12:06:23.049137+00:00"},{"alias_kind":"pith_short_8","alias_value":"HLL5LGE7","created_at":"2026-07-05T12:06:23.049137+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/HLL5LGE77ERVUTJ4RITNA4KDUL","json":"https://pith.science/pith/HLL5LGE77ERVUTJ4RITNA4KDUL.json","graph_json":"https://pith.science/api/pith-number/HLL5LGE77ERVUTJ4RITNA4KDUL/graph.json","events_json":"https://pith.science/api/pith-number/HLL5LGE77ERVUTJ4RITNA4KDUL/events.json","paper":"https://pith.science/paper/HLL5LGE7"},"agent_actions":{"view_html":"https://pith.science/pith/HLL5LGE77ERVUTJ4RITNA4KDUL","download_json":"https://pith.science/pith/HLL5LGE77ERVUTJ4RITNA4KDUL.json","view_paper":"https://pith.science/paper/HLL5LGE7","resolve_alias":"https://pith.science/api/pith-number/resolve?arxiv=2509.05732&json=true","fetch_graph":"https://pith.science/api/pith-number/HLL5LGE77ERVUTJ4RITNA4KDUL/graph.json","fetch_events":"https://pith.science/api/pith-number/HLL5LGE77ERVUTJ4RITNA4KDUL/events.json","actions":{"anchor_timestamp":"https://pith.science/pith/HLL5LGE77ERVUTJ4RITNA4KDUL/action/timestamp_anchor","attest_storage":"https://pith.science/pith/HLL5LGE77ERVUTJ4RITNA4KDUL/action/storage_attestation","attest_author":"https://pith.science/pith/HLL5LGE77ERVUTJ4RITNA4KDUL/action/author_attestation","sign_citation":"https://pith.science/pith/HLL5LGE77ERVUTJ4RITNA4KDUL/action/citation_signature","submit_replication":"https://pith.science/pith/HLL5LGE77ERVUTJ4RITNA4KDUL/action/replication_record"}},"created_at":"2026-07-05T12:06:23.049137+00:00","updated_at":"2026-07-05T12:06:23.049137+00:00"}