{"record_type":"pith_number_record","schema_url":"https://pith.science/schemas/pith-number/v1.json","pith_number":"pith:2019:FQKXOJSOBFWSEJCD7QQ7HREH6O","short_pith_number":"pith:FQKXOJSO","schema_version":"1.0","canonical_sha256":"2c1577264e096d222443fc21f3c487f39b7ee0ebde9af569e22d917905edc4cd","source":{"kind":"arxiv","id":"1906.01510","version":1},"attestation_state":"computed","paper":{"title":"Accelerating Physics-Based Simulations Using Neural Network Proxies: An Application in Oil Reservoir Modeling","license":"http://arxiv.org/licenses/nonexclusive-distrib/1.0/","headline":"","cross_cats":["stat.ML"],"primary_cat":"cs.LG","authors_text":"Alan King, Andres Codas, Georgios Kollias, Jesus Rios, Jiri Navratil, Ruben Torrado","submitted_at":"2019-05-23T20:09:13Z","abstract_excerpt":"We develop a proxy model based on deep learning methods to accelerate the simulations of oil reservoirs--by three orders of magnitude--compared to industry-strength physics-based PDE solvers. This paper describes a new architectural approach to this task, accompanied by a thorough experimental evaluation on a publicly available reservoir model. We demonstrate that in a practical setting a speedup of more than 2000X can be achieved with an average sequence error of about 10\\% relative to the oil-field simulator. The proxy model is contrasted with a high-quality physics-based acceleration baseli"},"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":"1906.01510","kind":"arxiv","version":1},"metadata":{"license":"http://arxiv.org/licenses/nonexclusive-distrib/1.0/","primary_cat":"cs.LG","submitted_at":"2019-05-23T20:09:13Z","cross_cats_sorted":["stat.ML"],"title_canon_sha256":"c9b12d4d5d5c94738fbb2a1f960d71bc95a51e23fce42050c65af70daf853a70","abstract_canon_sha256":"be76dfe4c4de9e03082993752625b31b3e2e74ad1abef8e2f4e7f1ac4009e369"},"schema_version":"1.0"},"receipt":{"kind":"pith_receipt","key_id":"pith-v1-2026-05","algorithm":"ed25519","signed_at":"2026-07-05T00:05:53.845523Z","signature_b64":"aBYEtZo6s06Z/2qse1MWpjCBdyB20azL63VXKcRTTOETOpabddsb7vI9px317MwEyghvIR/kYDPg22hq+fdQAw==","signed_message":"canonical_sha256_bytes","builder_version":"pith-number-builder-2026-05-17-v1","receipt_version":"0.3","canonical_sha256":"2c1577264e096d222443fc21f3c487f39b7ee0ebde9af569e22d917905edc4cd","last_reissued_at":"2026-07-05T00:05:53.845029Z","signature_status":"signed_v1","first_computed_at":"2026-07-05T00:05:53.845029Z","public_key_fingerprint":"8d4b5ee74e4693bcd1df2446408b0d54"},"graph_snapshot":{"paper":{"title":"Accelerating Physics-Based Simulations Using Neural Network Proxies: An Application in Oil Reservoir Modeling","license":"http://arxiv.org/licenses/nonexclusive-distrib/1.0/","headline":"","cross_cats":["stat.ML"],"primary_cat":"cs.LG","authors_text":"Alan King, Andres Codas, Georgios Kollias, Jesus Rios, Jiri Navratil, Ruben Torrado","submitted_at":"2019-05-23T20:09:13Z","abstract_excerpt":"We develop a proxy model based on deep learning methods to accelerate the simulations of oil reservoirs--by three orders of magnitude--compared to industry-strength physics-based PDE solvers. This paper describes a new architectural approach to this task, accompanied by a thorough experimental evaluation on a publicly available reservoir model. We demonstrate that in a practical setting a speedup of more than 2000X can be achieved with an average sequence error of about 10\\% relative to the oil-field simulator. The proxy model is contrasted with a high-quality physics-based acceleration baseli"},"claims":{"count":0,"items":[],"snapshot_sha256":"258153158e38e3291e3d48162225fcdb2d5a3ed65a07baac614ab91432fd4f57"},"source":{"id":"1906.01510","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/1906.01510/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":"1906.01510","created_at":"2026-07-05T00:05:53.845090+00:00"},{"alias_kind":"arxiv_version","alias_value":"1906.01510v1","created_at":"2026-07-05T00:05:53.845090+00:00"},{"alias_kind":"doi","alias_value":"10.48550/arxiv.1906.01510","created_at":"2026-07-05T00:05:53.845090+00:00"},{"alias_kind":"pith_short_12","alias_value":"FQKXOJSOBFWS","created_at":"2026-07-05T00:05:53.845090+00:00"},{"alias_kind":"pith_short_16","alias_value":"FQKXOJSOBFWSEJCD","created_at":"2026-07-05T00:05:53.845090+00:00"},{"alias_kind":"pith_short_8","alias_value":"FQKXOJSO","created_at":"2026-07-05T00:05:53.845090+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/FQKXOJSOBFWSEJCD7QQ7HREH6O","json":"https://pith.science/pith/FQKXOJSOBFWSEJCD7QQ7HREH6O.json","graph_json":"https://pith.science/api/pith-number/FQKXOJSOBFWSEJCD7QQ7HREH6O/graph.json","events_json":"https://pith.science/api/pith-number/FQKXOJSOBFWSEJCD7QQ7HREH6O/events.json","paper":"https://pith.science/paper/FQKXOJSO"},"agent_actions":{"view_html":"https://pith.science/pith/FQKXOJSOBFWSEJCD7QQ7HREH6O","download_json":"https://pith.science/pith/FQKXOJSOBFWSEJCD7QQ7HREH6O.json","view_paper":"https://pith.science/paper/FQKXOJSO","resolve_alias":"https://pith.science/api/pith-number/resolve?arxiv=1906.01510&json=true","fetch_graph":"https://pith.science/api/pith-number/FQKXOJSOBFWSEJCD7QQ7HREH6O/graph.json","fetch_events":"https://pith.science/api/pith-number/FQKXOJSOBFWSEJCD7QQ7HREH6O/events.json","actions":{"anchor_timestamp":"https://pith.science/pith/FQKXOJSOBFWSEJCD7QQ7HREH6O/action/timestamp_anchor","attest_storage":"https://pith.science/pith/FQKXOJSOBFWSEJCD7QQ7HREH6O/action/storage_attestation","attest_author":"https://pith.science/pith/FQKXOJSOBFWSEJCD7QQ7HREH6O/action/author_attestation","sign_citation":"https://pith.science/pith/FQKXOJSOBFWSEJCD7QQ7HREH6O/action/citation_signature","submit_replication":"https://pith.science/pith/FQKXOJSOBFWSEJCD7QQ7HREH6O/action/replication_record"}},"created_at":"2026-07-05T00:05:53.845090+00:00","updated_at":"2026-07-05T00:05:53.845090+00:00"}