{"record_type":"pith_number_record","schema_url":"https://pith.science/schemas/pith-number/v1.json","pith_number":"pith:2022:PU53GKDONSVZOVNBI3SEY32VJO","short_pith_number":"pith:PU53GKDO","schema_version":"1.0","canonical_sha256":"7d3bb3286e6cab9755a146e44c6f554b9eaba468867f1737b9547bbfed07016f","source":{"kind":"arxiv","id":"2211.02572","version":1},"attestation_state":"computed","paper":{"title":"Deep reinforcement learning for flow control exploits different physics for increasing Reynolds-number regimes","license":"http://creativecommons.org/licenses/by/4.0/","headline":"","cross_cats":[],"primary_cat":"physics.flu-dyn","authors_text":"Arnau Mir\\'o, Bernat Font, Francisco Alc\\'antara-\\'Avila, Jean Rabault, Luis Miguel Garc\\'ia-Cuevas, Oriol Lehmkuhl, Pau Varela, Pol Su\\'arez, Ricardo Vinuesa","submitted_at":"2022-11-04T16:42:58Z","abstract_excerpt":"Deep artificial neural networks (ANNs) used together with deep reinforcement learning (DRL) are receiving growing attention due to their capabilities to control complex problems. This technique has been recently used to solve problems related to flow control. In this work, an ANN trained through a DRL agent is used to perform active flow control. Two-dimensional simulations of the flow around a cylinder are conducted and an active control based on two jets located on the walls of the cylinder is considered. By gathering information from the flow surrounding the cylinder, the ANN agent is able "},"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":"2211.02572","kind":"arxiv","version":1},"metadata":{"license":"http://creativecommons.org/licenses/by/4.0/","primary_cat":"physics.flu-dyn","submitted_at":"2022-11-04T16:42:58Z","cross_cats_sorted":[],"title_canon_sha256":"7f9dd57176289933828b3079b2b140bb784480c34c1020fa960b73fb2dcbfbc0","abstract_canon_sha256":"0da12ff45ae605f9a01464c76061a526a26603f708c614c02ee8cbaff9449c34"},"schema_version":"1.0"},"receipt":{"kind":"pith_receipt","key_id":"pith-v1-2026-05","algorithm":"ed25519","signed_at":"2026-07-05T05:13:16.782213Z","signature_b64":"BPRS1oXeBnfo1YG/u7LU6rjRv7WKLSY/anjnrn361+cY1/GZi+Bsb2x+IPUxFRj+fxBDjJQ8ZhMJL/BW2pRxDw==","signed_message":"canonical_sha256_bytes","builder_version":"pith-number-builder-2026-05-17-v1","receipt_version":"0.3","canonical_sha256":"7d3bb3286e6cab9755a146e44c6f554b9eaba468867f1737b9547bbfed07016f","last_reissued_at":"2026-07-05T05:13:16.781744Z","signature_status":"signed_v1","first_computed_at":"2026-07-05T05:13:16.781744Z","public_key_fingerprint":"8d4b5ee74e4693bcd1df2446408b0d54"},"graph_snapshot":{"paper":{"title":"Deep reinforcement learning for flow control exploits different physics for increasing Reynolds-number regimes","license":"http://creativecommons.org/licenses/by/4.0/","headline":"","cross_cats":[],"primary_cat":"physics.flu-dyn","authors_text":"Arnau Mir\\'o, Bernat Font, Francisco Alc\\'antara-\\'Avila, Jean Rabault, Luis Miguel Garc\\'ia-Cuevas, Oriol Lehmkuhl, Pau Varela, Pol Su\\'arez, Ricardo Vinuesa","submitted_at":"2022-11-04T16:42:58Z","abstract_excerpt":"Deep artificial neural networks (ANNs) used together with deep reinforcement learning (DRL) are receiving growing attention due to their capabilities to control complex problems. This technique has been recently used to solve problems related to flow control. In this work, an ANN trained through a DRL agent is used to perform active flow control. Two-dimensional simulations of the flow around a cylinder are conducted and an active control based on two jets located on the walls of the cylinder is considered. By gathering information from the flow surrounding the cylinder, the ANN agent is able "},"claims":{"count":0,"items":[],"snapshot_sha256":"258153158e38e3291e3d48162225fcdb2d5a3ed65a07baac614ab91432fd4f57"},"source":{"id":"2211.02572","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/2211.02572/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":"2211.02572","created_at":"2026-07-05T05:13:16.781802+00:00"},{"alias_kind":"arxiv_version","alias_value":"2211.02572v1","created_at":"2026-07-05T05:13:16.781802+00:00"},{"alias_kind":"doi","alias_value":"10.48550/arxiv.2211.02572","created_at":"2026-07-05T05:13:16.781802+00:00"},{"alias_kind":"pith_short_12","alias_value":"PU53GKDONSVZ","created_at":"2026-07-05T05:13:16.781802+00:00"},{"alias_kind":"pith_short_16","alias_value":"PU53GKDONSVZOVNB","created_at":"2026-07-05T05:13:16.781802+00:00"},{"alias_kind":"pith_short_8","alias_value":"PU53GKDO","created_at":"2026-07-05T05:13:16.781802+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/PU53GKDONSVZOVNBI3SEY32VJO","json":"https://pith.science/pith/PU53GKDONSVZOVNBI3SEY32VJO.json","graph_json":"https://pith.science/api/pith-number/PU53GKDONSVZOVNBI3SEY32VJO/graph.json","events_json":"https://pith.science/api/pith-number/PU53GKDONSVZOVNBI3SEY32VJO/events.json","paper":"https://pith.science/paper/PU53GKDO"},"agent_actions":{"view_html":"https://pith.science/pith/PU53GKDONSVZOVNBI3SEY32VJO","download_json":"https://pith.science/pith/PU53GKDONSVZOVNBI3SEY32VJO.json","view_paper":"https://pith.science/paper/PU53GKDO","resolve_alias":"https://pith.science/api/pith-number/resolve?arxiv=2211.02572&json=true","fetch_graph":"https://pith.science/api/pith-number/PU53GKDONSVZOVNBI3SEY32VJO/graph.json","fetch_events":"https://pith.science/api/pith-number/PU53GKDONSVZOVNBI3SEY32VJO/events.json","actions":{"anchor_timestamp":"https://pith.science/pith/PU53GKDONSVZOVNBI3SEY32VJO/action/timestamp_anchor","attest_storage":"https://pith.science/pith/PU53GKDONSVZOVNBI3SEY32VJO/action/storage_attestation","attest_author":"https://pith.science/pith/PU53GKDONSVZOVNBI3SEY32VJO/action/author_attestation","sign_citation":"https://pith.science/pith/PU53GKDONSVZOVNBI3SEY32VJO/action/citation_signature","submit_replication":"https://pith.science/pith/PU53GKDONSVZOVNBI3SEY32VJO/action/replication_record"}},"created_at":"2026-07-05T05:13:16.781802+00:00","updated_at":"2026-07-05T05:13:16.781802+00:00"}