{"record_type":"pith_number_record","schema_url":"https://pith.science/schemas/pith-number/v1.json","pith_number":"pith:2024:GDXMXLO3S56Q5GDG4AUQZB6DW6","short_pith_number":"pith:GDXMXLO3","schema_version":"1.0","canonical_sha256":"30eecbaddb977d0e9866e0290c87c3b7b299dcca9bb50dc884ff9aeb3392d0c9","source":{"kind":"arxiv","id":"2408.03450","version":1},"attestation_state":"computed","paper":{"title":"Probabilistic Surrogate Model for Accelerating the Design of Electric Vehicle Battery Enclosures for Crash Performance","license":"http://creativecommons.org/licenses/by/4.0/","headline":"","cross_cats":["cs.CE"],"primary_cat":"cs.LG","authors_text":"Ayoub Soulami, Harish Cherukuri, Kranthi Balusu, Ram Devanathan, Shadab Anwar Shaikh","submitted_at":"2024-08-06T21:03:16Z","abstract_excerpt":"This paper presents a probabilistic surrogate model for the accelerated design of electric vehicle battery enclosures with a focus on crash performance. The study integrates high-throughput finite element simulations and Gaussian Process Regression to develop a surrogate model that predicts crash parameters with high accuracy while providing uncertainty estimates. The model was trained using data generated from thermoforming and crash simulations over a range of material and process parameters. Validation against new simulation data demonstrated the model's predictive accuracy with mean absolu"},"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":"2408.03450","kind":"arxiv","version":1},"metadata":{"license":"http://creativecommons.org/licenses/by/4.0/","primary_cat":"cs.LG","submitted_at":"2024-08-06T21:03:16Z","cross_cats_sorted":["cs.CE"],"title_canon_sha256":"4b1f91824bc8a99b5fa869f792e304900ff95929449891cd8d5b1b87d2e0ba3c","abstract_canon_sha256":"7c9f07514ca9420f2cd01fd1fc9618444627de36f427a39bf2811d9e520c22bc"},"schema_version":"1.0"},"receipt":{"kind":"pith_receipt","key_id":"pith-v1-2026-05","algorithm":"ed25519","signed_at":"2026-07-05T08:52:58.783908Z","signature_b64":"kwilK/yrAGE32KAWrqYIOxKuu1EuHWipO8iduavMLienVhvBdnqZb0945fjm8iCdNgO0oMqWfLUujyKLS0/LAw==","signed_message":"canonical_sha256_bytes","builder_version":"pith-number-builder-2026-05-17-v1","receipt_version":"0.3","canonical_sha256":"30eecbaddb977d0e9866e0290c87c3b7b299dcca9bb50dc884ff9aeb3392d0c9","last_reissued_at":"2026-07-05T08:52:58.783481Z","signature_status":"signed_v1","first_computed_at":"2026-07-05T08:52:58.783481Z","public_key_fingerprint":"8d4b5ee74e4693bcd1df2446408b0d54"},"graph_snapshot":{"paper":{"title":"Probabilistic Surrogate Model for Accelerating the Design of Electric Vehicle Battery Enclosures for Crash Performance","license":"http://creativecommons.org/licenses/by/4.0/","headline":"","cross_cats":["cs.CE"],"primary_cat":"cs.LG","authors_text":"Ayoub Soulami, Harish Cherukuri, Kranthi Balusu, Ram Devanathan, Shadab Anwar Shaikh","submitted_at":"2024-08-06T21:03:16Z","abstract_excerpt":"This paper presents a probabilistic surrogate model for the accelerated design of electric vehicle battery enclosures with a focus on crash performance. The study integrates high-throughput finite element simulations and Gaussian Process Regression to develop a surrogate model that predicts crash parameters with high accuracy while providing uncertainty estimates. The model was trained using data generated from thermoforming and crash simulations over a range of material and process parameters. Validation against new simulation data demonstrated the model's predictive accuracy with mean absolu"},"claims":{"count":0,"items":[],"snapshot_sha256":"258153158e38e3291e3d48162225fcdb2d5a3ed65a07baac614ab91432fd4f57"},"source":{"id":"2408.03450","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/2408.03450/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":"2408.03450","created_at":"2026-07-05T08:52:58.783536+00:00"},{"alias_kind":"arxiv_version","alias_value":"2408.03450v1","created_at":"2026-07-05T08:52:58.783536+00:00"},{"alias_kind":"doi","alias_value":"10.48550/arxiv.2408.03450","created_at":"2026-07-05T08:52:58.783536+00:00"},{"alias_kind":"pith_short_12","alias_value":"GDXMXLO3S56Q","created_at":"2026-07-05T08:52:58.783536+00:00"},{"alias_kind":"pith_short_16","alias_value":"GDXMXLO3S56Q5GDG","created_at":"2026-07-05T08:52:58.783536+00:00"},{"alias_kind":"pith_short_8","alias_value":"GDXMXLO3","created_at":"2026-07-05T08:52:58.783536+00:00"}],"events":[],"event_summary":{},"paper_claims":[],"inbound_citations":{"count":1,"internal_anchor_count":1,"sample":[{"citing_arxiv_id":"2506.08205","citing_title":"A Machine Learning Approach to Generate Residual Stress Distributions using Sparse Characterization Data in Friction-Stir Processed Parts","ref_index":26,"is_internal_anchor":true}]},"formal_canon":{"evidence_count":0,"sample":[],"anchors":[]},"links":{"html":"https://pith.science/pith/GDXMXLO3S56Q5GDG4AUQZB6DW6","json":"https://pith.science/pith/GDXMXLO3S56Q5GDG4AUQZB6DW6.json","graph_json":"https://pith.science/api/pith-number/GDXMXLO3S56Q5GDG4AUQZB6DW6/graph.json","events_json":"https://pith.science/api/pith-number/GDXMXLO3S56Q5GDG4AUQZB6DW6/events.json","paper":"https://pith.science/paper/GDXMXLO3"},"agent_actions":{"view_html":"https://pith.science/pith/GDXMXLO3S56Q5GDG4AUQZB6DW6","download_json":"https://pith.science/pith/GDXMXLO3S56Q5GDG4AUQZB6DW6.json","view_paper":"https://pith.science/paper/GDXMXLO3","resolve_alias":"https://pith.science/api/pith-number/resolve?arxiv=2408.03450&json=true","fetch_graph":"https://pith.science/api/pith-number/GDXMXLO3S56Q5GDG4AUQZB6DW6/graph.json","fetch_events":"https://pith.science/api/pith-number/GDXMXLO3S56Q5GDG4AUQZB6DW6/events.json","actions":{"anchor_timestamp":"https://pith.science/pith/GDXMXLO3S56Q5GDG4AUQZB6DW6/action/timestamp_anchor","attest_storage":"https://pith.science/pith/GDXMXLO3S56Q5GDG4AUQZB6DW6/action/storage_attestation","attest_author":"https://pith.science/pith/GDXMXLO3S56Q5GDG4AUQZB6DW6/action/author_attestation","sign_citation":"https://pith.science/pith/GDXMXLO3S56Q5GDG4AUQZB6DW6/action/citation_signature","submit_replication":"https://pith.science/pith/GDXMXLO3S56Q5GDG4AUQZB6DW6/action/replication_record"}},"created_at":"2026-07-05T08:52:58.783536+00:00","updated_at":"2026-07-05T08:52:58.783536+00:00"}