{"record_type":"pith_number_record","schema_url":"https://pith.science/schemas/pith-number/v1.json","pith_number":"pith:2023:V7ODUO6CHUD3IICV4CDVA4MW5Q","short_pith_number":"pith:V7ODUO6C","schema_version":"1.0","canonical_sha256":"afdc3a3bc23d07b42055e087507196ec15c96e868011199beca1d10d5decdaae","source":{"kind":"arxiv","id":"2305.01122","version":1},"attestation_state":"computed","paper":{"title":"Learning Controllable Adaptive Simulation for Multi-resolution Physics","license":"http://creativecommons.org/licenses/by/4.0/","headline":"","cross_cats":["cs.CE"],"primary_cat":"cs.LG","authors_text":"Gordon Wetzstein, Jure Leskovec, Qingqing Zhao, Tailin Wu, Takashi Maruyama","submitted_at":"2023-05-01T23:20:27Z","abstract_excerpt":"Simulating the time evolution of physical systems is pivotal in many scientific and engineering problems. An open challenge in simulating such systems is their multi-resolution dynamics: a small fraction of the system is extremely dynamic, and requires very fine-grained resolution, while a majority of the system is changing slowly and can be modeled by coarser spatial scales. Typical learning-based surrogate models use a uniform spatial scale, which needs to resolve to the finest required scale and can waste a huge compute to achieve required accuracy. In this work, we introduce Learning contr"},"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":"2305.01122","kind":"arxiv","version":1},"metadata":{"license":"http://creativecommons.org/licenses/by/4.0/","primary_cat":"cs.LG","submitted_at":"2023-05-01T23:20:27Z","cross_cats_sorted":["cs.CE"],"title_canon_sha256":"790b181c04f1b3ae85984fa702d1dc6f5bc6c7aa404bd2d8e12e15883ebdead0","abstract_canon_sha256":"c194b736a98b5f4525961005aa13d8100245bbe213cb7015837deebdb19ed57a"},"schema_version":"1.0"},"receipt":{"kind":"pith_receipt","key_id":"pith-v1-2026-05","algorithm":"ed25519","signed_at":"2026-07-05T06:06:21.739297Z","signature_b64":"mYXv6srnR1fhyN7t3+gOZZ5GI/sx06l/kMSdXSwkl9IfneB+4bu8Xuaxw0T8p9wT4J8rHpQobUTAKLw/44ahDQ==","signed_message":"canonical_sha256_bytes","builder_version":"pith-number-builder-2026-05-17-v1","receipt_version":"0.3","canonical_sha256":"afdc3a3bc23d07b42055e087507196ec15c96e868011199beca1d10d5decdaae","last_reissued_at":"2026-07-05T06:06:21.738826Z","signature_status":"signed_v1","first_computed_at":"2026-07-05T06:06:21.738826Z","public_key_fingerprint":"8d4b5ee74e4693bcd1df2446408b0d54"},"graph_snapshot":{"paper":{"title":"Learning Controllable Adaptive Simulation for Multi-resolution Physics","license":"http://creativecommons.org/licenses/by/4.0/","headline":"","cross_cats":["cs.CE"],"primary_cat":"cs.LG","authors_text":"Gordon Wetzstein, Jure Leskovec, Qingqing Zhao, Tailin Wu, Takashi Maruyama","submitted_at":"2023-05-01T23:20:27Z","abstract_excerpt":"Simulating the time evolution of physical systems is pivotal in many scientific and engineering problems. An open challenge in simulating such systems is their multi-resolution dynamics: a small fraction of the system is extremely dynamic, and requires very fine-grained resolution, while a majority of the system is changing slowly and can be modeled by coarser spatial scales. Typical learning-based surrogate models use a uniform spatial scale, which needs to resolve to the finest required scale and can waste a huge compute to achieve required accuracy. In this work, we introduce Learning contr"},"claims":{"count":0,"items":[],"snapshot_sha256":"258153158e38e3291e3d48162225fcdb2d5a3ed65a07baac614ab91432fd4f57"},"source":{"id":"2305.01122","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/2305.01122/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":"2305.01122","created_at":"2026-07-05T06:06:21.738889+00:00"},{"alias_kind":"arxiv_version","alias_value":"2305.01122v1","created_at":"2026-07-05T06:06:21.738889+00:00"},{"alias_kind":"doi","alias_value":"10.48550/arxiv.2305.01122","created_at":"2026-07-05T06:06:21.738889+00:00"},{"alias_kind":"pith_short_12","alias_value":"V7ODUO6CHUD3","created_at":"2026-07-05T06:06:21.738889+00:00"},{"alias_kind":"pith_short_16","alias_value":"V7ODUO6CHUD3IICV","created_at":"2026-07-05T06:06:21.738889+00:00"},{"alias_kind":"pith_short_8","alias_value":"V7ODUO6C","created_at":"2026-07-05T06:06:21.738889+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/V7ODUO6CHUD3IICV4CDVA4MW5Q","json":"https://pith.science/pith/V7ODUO6CHUD3IICV4CDVA4MW5Q.json","graph_json":"https://pith.science/api/pith-number/V7ODUO6CHUD3IICV4CDVA4MW5Q/graph.json","events_json":"https://pith.science/api/pith-number/V7ODUO6CHUD3IICV4CDVA4MW5Q/events.json","paper":"https://pith.science/paper/V7ODUO6C"},"agent_actions":{"view_html":"https://pith.science/pith/V7ODUO6CHUD3IICV4CDVA4MW5Q","download_json":"https://pith.science/pith/V7ODUO6CHUD3IICV4CDVA4MW5Q.json","view_paper":"https://pith.science/paper/V7ODUO6C","resolve_alias":"https://pith.science/api/pith-number/resolve?arxiv=2305.01122&json=true","fetch_graph":"https://pith.science/api/pith-number/V7ODUO6CHUD3IICV4CDVA4MW5Q/graph.json","fetch_events":"https://pith.science/api/pith-number/V7ODUO6CHUD3IICV4CDVA4MW5Q/events.json","actions":{"anchor_timestamp":"https://pith.science/pith/V7ODUO6CHUD3IICV4CDVA4MW5Q/action/timestamp_anchor","attest_storage":"https://pith.science/pith/V7ODUO6CHUD3IICV4CDVA4MW5Q/action/storage_attestation","attest_author":"https://pith.science/pith/V7ODUO6CHUD3IICV4CDVA4MW5Q/action/author_attestation","sign_citation":"https://pith.science/pith/V7ODUO6CHUD3IICV4CDVA4MW5Q/action/citation_signature","submit_replication":"https://pith.science/pith/V7ODUO6CHUD3IICV4CDVA4MW5Q/action/replication_record"}},"created_at":"2026-07-05T06:06:21.738889+00:00","updated_at":"2026-07-05T06:06:21.738889+00:00"}