{"record_type":"pith_number_record","schema_url":"https://pith.science/schemas/pith-number/v1.json","pith_number":"pith:2018:2MU6OSSTOHSOLHLS7ERZCI7RKX","short_pith_number":"pith:2MU6OSST","schema_version":"1.0","canonical_sha256":"d329e74a5371e4e59d72f9239123f155c88d0300b00426cd401114f635d17c85","source":{"kind":"arxiv","id":"1812.09444","version":1},"attestation_state":"computed","paper":{"title":"Deep autoregressive neural networks for high-dimensional inverse problems in groundwater contaminant source identification","license":"http://arxiv.org/licenses/nonexclusive-distrib/1.0/","headline":"","cross_cats":["cs.LG"],"primary_cat":"stat.ML","authors_text":"Jichun Wu, Nicholas Zabaras, Shaoxing Mo, Xiaoqing Shi","submitted_at":"2018-12-22T03:46:41Z","abstract_excerpt":"Identification of a groundwater contaminant source simultaneously with the hydraulic conductivity in highly-heterogeneous media often results in a high-dimensional inverse problem. In this study, a deep autoregressive neural network-based surrogate method is developed for the forward model to allow us to solve efficiently such high-dimensional inverse problems. The surrogate is trained using limited evaluations of the forward model. Since the relationship between the time-varying inputs and outputs of the forward transport model is complex, we propose an autoregressive strategy, which treats t"},"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":"1812.09444","kind":"arxiv","version":1},"metadata":{"license":"http://arxiv.org/licenses/nonexclusive-distrib/1.0/","primary_cat":"stat.ML","submitted_at":"2018-12-22T03:46:41Z","cross_cats_sorted":["cs.LG"],"title_canon_sha256":"92610e49ffdf3201ff47670cb78dc0ab6c819b4a703c8e832d727829eeb9c18b","abstract_canon_sha256":"124840ddc71823de50504c3fce28f024216d2f3bf891b6d4158ada196a7f996f"},"schema_version":"1.0"},"receipt":{"kind":"pith_receipt","key_id":"pith-v1-2026-05","algorithm":"ed25519","signed_at":"2026-05-17T23:51:09.421506Z","signature_b64":"qmNSc/zwZhxs8QtUJGh5MrjUMetijxTvAMaQwqJ3Oeh+nd1kvb4kRlWG7uXAOVlmD4ZaxdD2VGTI9DJnpn3iBA==","signed_message":"canonical_sha256_bytes","builder_version":"pith-number-builder-2026-05-17-v1","receipt_version":"0.3","canonical_sha256":"d329e74a5371e4e59d72f9239123f155c88d0300b00426cd401114f635d17c85","last_reissued_at":"2026-05-17T23:51:09.420872Z","signature_status":"signed_v1","first_computed_at":"2026-05-17T23:51:09.420872Z","public_key_fingerprint":"8d4b5ee74e4693bcd1df2446408b0d54"},"graph_snapshot":{"paper":{"title":"Deep autoregressive neural networks for high-dimensional inverse problems in groundwater contaminant source identification","license":"http://arxiv.org/licenses/nonexclusive-distrib/1.0/","headline":"","cross_cats":["cs.LG"],"primary_cat":"stat.ML","authors_text":"Jichun Wu, Nicholas Zabaras, Shaoxing Mo, Xiaoqing Shi","submitted_at":"2018-12-22T03:46:41Z","abstract_excerpt":"Identification of a groundwater contaminant source simultaneously with the hydraulic conductivity in highly-heterogeneous media often results in a high-dimensional inverse problem. In this study, a deep autoregressive neural network-based surrogate method is developed for the forward model to allow us to solve efficiently such high-dimensional inverse problems. The surrogate is trained using limited evaluations of the forward model. Since the relationship between the time-varying inputs and outputs of the forward transport model is complex, we propose an autoregressive strategy, which treats t"},"claims":{"count":0,"items":[],"snapshot_sha256":"258153158e38e3291e3d48162225fcdb2d5a3ed65a07baac614ab91432fd4f57"},"source":{"id":"1812.09444","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":""},"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":"1812.09444","created_at":"2026-05-17T23:51:09.420979+00:00"},{"alias_kind":"arxiv_version","alias_value":"1812.09444v1","created_at":"2026-05-17T23:51:09.420979+00:00"},{"alias_kind":"doi","alias_value":"10.48550/arxiv.1812.09444","created_at":"2026-05-17T23:51:09.420979+00:00"},{"alias_kind":"pith_short_12","alias_value":"2MU6OSSTOHSO","created_at":"2026-05-18T12:32:02.567920+00:00"},{"alias_kind":"pith_short_16","alias_value":"2MU6OSSTOHSOLHLS","created_at":"2026-05-18T12:32:02.567920+00:00"},{"alias_kind":"pith_short_8","alias_value":"2MU6OSST","created_at":"2026-05-18T12:32:02.567920+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/2MU6OSSTOHSOLHLS7ERZCI7RKX","json":"https://pith.science/pith/2MU6OSSTOHSOLHLS7ERZCI7RKX.json","graph_json":"https://pith.science/api/pith-number/2MU6OSSTOHSOLHLS7ERZCI7RKX/graph.json","events_json":"https://pith.science/api/pith-number/2MU6OSSTOHSOLHLS7ERZCI7RKX/events.json","paper":"https://pith.science/paper/2MU6OSST"},"agent_actions":{"view_html":"https://pith.science/pith/2MU6OSSTOHSOLHLS7ERZCI7RKX","download_json":"https://pith.science/pith/2MU6OSSTOHSOLHLS7ERZCI7RKX.json","view_paper":"https://pith.science/paper/2MU6OSST","resolve_alias":"https://pith.science/api/pith-number/resolve?arxiv=1812.09444&json=true","fetch_graph":"https://pith.science/api/pith-number/2MU6OSSTOHSOLHLS7ERZCI7RKX/graph.json","fetch_events":"https://pith.science/api/pith-number/2MU6OSSTOHSOLHLS7ERZCI7RKX/events.json","actions":{"anchor_timestamp":"https://pith.science/pith/2MU6OSSTOHSOLHLS7ERZCI7RKX/action/timestamp_anchor","attest_storage":"https://pith.science/pith/2MU6OSSTOHSOLHLS7ERZCI7RKX/action/storage_attestation","attest_author":"https://pith.science/pith/2MU6OSSTOHSOLHLS7ERZCI7RKX/action/author_attestation","sign_citation":"https://pith.science/pith/2MU6OSSTOHSOLHLS7ERZCI7RKX/action/citation_signature","submit_replication":"https://pith.science/pith/2MU6OSSTOHSOLHLS7ERZCI7RKX/action/replication_record"}},"created_at":"2026-05-17T23:51:09.420979+00:00","updated_at":"2026-05-17T23:51:09.420979+00:00"}