{"record_type":"pith_number_record","schema_url":"https://pith.science/schemas/pith-number/v1.json","pith_number":"pith:2025:CVBOM46GH6FR522PJGMXZKUTVP","short_pith_number":"pith:CVBOM46G","schema_version":"1.0","canonical_sha256":"1542e673c63f8b1eeb4f49997caa93abeae413d55780600b03b90e8c7e54812e","source":{"kind":"arxiv","id":"2504.12503","version":1},"attestation_state":"computed","paper":{"title":"Continual Learning Strategies for 3D Engineering Regression Problems: A Benchmarking Study","license":"http://creativecommons.org/licenses/by/4.0/","headline":"","cross_cats":["cs.AI","cs.CE"],"primary_cat":"cs.LG","authors_text":"Faez Ahmed, Kaira M. Samuel","submitted_at":"2025-04-16T21:40:03Z","abstract_excerpt":"Engineering problems that apply machine learning often involve computationally intensive methods but rely on limited datasets. As engineering data evolves with new designs and constraints, models must incorporate new knowledge over time. However, high computational costs make retraining models from scratch infeasible. Continual learning (CL) offers a promising solution by enabling models to learn from sequential data while mitigating catastrophic forgetting, where a model forgets previously learned mappings. This work introduces CL to engineering design by benchmarking several CL methods on re"},"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":"2504.12503","kind":"arxiv","version":1},"metadata":{"license":"http://creativecommons.org/licenses/by/4.0/","primary_cat":"cs.LG","submitted_at":"2025-04-16T21:40:03Z","cross_cats_sorted":["cs.AI","cs.CE"],"title_canon_sha256":"c581fb8fd4da2fb4bc90afddea73464bb4e20692678f9dd6d32da7af0047517e","abstract_canon_sha256":"a3c2abe240925c6e3105d10448a3922cd187c7175332817dea3bb0a08d0a3c10"},"schema_version":"1.0"},"receipt":{"kind":"pith_receipt","key_id":"pith-v1-2026-05","algorithm":"ed25519","signed_at":"2026-07-05T10:50:17.378670Z","signature_b64":"DGl1elUYDqU+A0x0+zh9ftJpK67aqVZbjcaLFY9Q0tycmjVbGHyJP3h9Jx6sKf3vWEoWn7PjMe6Dy1gffJakDQ==","signed_message":"canonical_sha256_bytes","builder_version":"pith-number-builder-2026-05-17-v1","receipt_version":"0.3","canonical_sha256":"1542e673c63f8b1eeb4f49997caa93abeae413d55780600b03b90e8c7e54812e","last_reissued_at":"2026-07-05T10:50:17.378243Z","signature_status":"signed_v1","first_computed_at":"2026-07-05T10:50:17.378243Z","public_key_fingerprint":"8d4b5ee74e4693bcd1df2446408b0d54"},"graph_snapshot":{"paper":{"title":"Continual Learning Strategies for 3D Engineering Regression Problems: A Benchmarking Study","license":"http://creativecommons.org/licenses/by/4.0/","headline":"","cross_cats":["cs.AI","cs.CE"],"primary_cat":"cs.LG","authors_text":"Faez Ahmed, Kaira M. Samuel","submitted_at":"2025-04-16T21:40:03Z","abstract_excerpt":"Engineering problems that apply machine learning often involve computationally intensive methods but rely on limited datasets. As engineering data evolves with new designs and constraints, models must incorporate new knowledge over time. However, high computational costs make retraining models from scratch infeasible. Continual learning (CL) offers a promising solution by enabling models to learn from sequential data while mitigating catastrophic forgetting, where a model forgets previously learned mappings. This work introduces CL to engineering design by benchmarking several CL methods on re"},"claims":{"count":0,"items":[],"snapshot_sha256":"258153158e38e3291e3d48162225fcdb2d5a3ed65a07baac614ab91432fd4f57"},"source":{"id":"2504.12503","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/2504.12503/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":"2504.12503","created_at":"2026-07-05T10:50:17.378307+00:00"},{"alias_kind":"arxiv_version","alias_value":"2504.12503v1","created_at":"2026-07-05T10:50:17.378307+00:00"},{"alias_kind":"doi","alias_value":"10.48550/arxiv.2504.12503","created_at":"2026-07-05T10:50:17.378307+00:00"},{"alias_kind":"pith_short_12","alias_value":"CVBOM46GH6FR","created_at":"2026-07-05T10:50:17.378307+00:00"},{"alias_kind":"pith_short_16","alias_value":"CVBOM46GH6FR522P","created_at":"2026-07-05T10:50:17.378307+00:00"},{"alias_kind":"pith_short_8","alias_value":"CVBOM46G","created_at":"2026-07-05T10:50:17.378307+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/CVBOM46GH6FR522PJGMXZKUTVP","json":"https://pith.science/pith/CVBOM46GH6FR522PJGMXZKUTVP.json","graph_json":"https://pith.science/api/pith-number/CVBOM46GH6FR522PJGMXZKUTVP/graph.json","events_json":"https://pith.science/api/pith-number/CVBOM46GH6FR522PJGMXZKUTVP/events.json","paper":"https://pith.science/paper/CVBOM46G"},"agent_actions":{"view_html":"https://pith.science/pith/CVBOM46GH6FR522PJGMXZKUTVP","download_json":"https://pith.science/pith/CVBOM46GH6FR522PJGMXZKUTVP.json","view_paper":"https://pith.science/paper/CVBOM46G","resolve_alias":"https://pith.science/api/pith-number/resolve?arxiv=2504.12503&json=true","fetch_graph":"https://pith.science/api/pith-number/CVBOM46GH6FR522PJGMXZKUTVP/graph.json","fetch_events":"https://pith.science/api/pith-number/CVBOM46GH6FR522PJGMXZKUTVP/events.json","actions":{"anchor_timestamp":"https://pith.science/pith/CVBOM46GH6FR522PJGMXZKUTVP/action/timestamp_anchor","attest_storage":"https://pith.science/pith/CVBOM46GH6FR522PJGMXZKUTVP/action/storage_attestation","attest_author":"https://pith.science/pith/CVBOM46GH6FR522PJGMXZKUTVP/action/author_attestation","sign_citation":"https://pith.science/pith/CVBOM46GH6FR522PJGMXZKUTVP/action/citation_signature","submit_replication":"https://pith.science/pith/CVBOM46GH6FR522PJGMXZKUTVP/action/replication_record"}},"created_at":"2026-07-05T10:50:17.378307+00:00","updated_at":"2026-07-05T10:50:17.378307+00:00"}