{"state_type":"pith_open_graph_state","state_version":"1.0","pith_number":"pith:2021:7MGCPDVOTBMXTTLKBWAXVWGDNE","merge_version":"pith-open-graph-merge-v1","event_count":2,"valid_event_count":2,"invalid_event_count":0,"equivocation_count":0,"current":{"canonical_record":{"metadata":{"abstract_canon_sha256":"2ec0de1c56b5e75cbc4c2b9c860a4352e72eef2d5531270140f976e38ba140fb","cross_cats_sorted":["cs.NA","math.NA"],"license":"http://arxiv.org/licenses/nonexclusive-distrib/1.0/","primary_cat":"cs.LG","submitted_at":"2021-03-01T22:55:48Z","title_canon_sha256":"3a74fa20a67a0c1ba1f76c0dbf37819a1c64d23b5737baf77f8da7ab1fabb2a1"},"schema_version":"1.0","source":{"id":"2103.01342","kind":"arxiv","version":3}},"source_aliases":[{"alias_kind":"arxiv","alias_value":"2103.01342","created_at":"2026-07-05T05:44:23Z"},{"alias_kind":"arxiv_version","alias_value":"2103.01342v3","created_at":"2026-07-05T05:44:23Z"},{"alias_kind":"doi","alias_value":"10.48550/arxiv.2103.01342","created_at":"2026-07-05T05:44:23Z"},{"alias_kind":"pith_short_12","alias_value":"7MGCPDVOTBMX","created_at":"2026-07-05T05:44:23Z"},{"alias_kind":"pith_short_16","alias_value":"7MGCPDVOTBMXTTLK","created_at":"2026-07-05T05:44:23Z"},{"alias_kind":"pith_short_8","alias_value":"7MGCPDVO","created_at":"2026-07-05T05:44:23Z"}],"graph_snapshots":[{"event_id":"sha256:8b2a69ddfe2e120508dd583da944038c36666f545deaf206fde29a676b97903c","target":"graph","created_at":"2026-07-05T05:44:23Z","signer":{"key_id":"pith-v1-2026-05","public_key_fingerprint":"8d4b5ee74e4693bcd1df2446408b0d54","signer_id":"pith.science","signer_type":"pith_registry"},"payload":{"graph_snapshot":{"author_claims":{"count":0,"snapshot_sha256":"258153158e38e3291e3d48162225fcdb2d5a3ed65a07baac614ab91432fd4f57","strong_count":0},"builder_version":"pith-number-builder-2026-05-17-v1","claims":{"count":0,"items":[],"snapshot_sha256":"258153158e38e3291e3d48162225fcdb2d5a3ed65a07baac614ab91432fd4f57"},"formal_canon":{"evidence_count":0,"snapshot_sha256":"258153158e38e3291e3d48162225fcdb2d5a3ed65a07baac614ab91432fd4f57"},"integrity":{"available":true,"clean":true,"detectors_run":[],"endpoint":"/pith/2103.01342/integrity.json","findings":[],"snapshot_sha256":"c28c3603d3b5d939e8dc4c7e95fa8dfce3d595e45f758748cecf8e644a296938","summary":{"advisory":0,"by_detector":{},"critical":0,"informational":0}},"paper":{"abstract_excerpt":"Large-scale finite element simulations of complex physical systems governed by partial differential equations (PDE) crucially depend on adaptive mesh refinement (AMR) to allocate computational budget to regions where higher resolution is required. Existing scalable AMR methods make heuristic refinement decisions based on instantaneous error estimation and thus do not aim for long-term optimality over an entire simulation. We propose a novel formulation of AMR as a Markov decision process and apply deep reinforcement learning (RL) to train refinement policies directly from simulation. AMR poses","authors_text":"Brenden Petersen, Daniel Faissol, Hongyuan Zha, Jean-Sylvain Camier, Jiachen Yang, Jun Kudo, Ketan Mittal, Robert Anderson, Tarik Dzanic, Tuo Zhao, Tzanio Kolev, Vladimir Tomov","cross_cats":["cs.NA","math.NA"],"headline":"","license":"http://arxiv.org/licenses/nonexclusive-distrib/1.0/","primary_cat":"cs.LG","submitted_at":"2021-03-01T22:55:48Z","title":"Reinforcement Learning for Adaptive Mesh Refinement"},"references":{"count":0,"internal_anchors":0,"resolved_work":0,"sample":[],"snapshot_sha256":"258153158e38e3291e3d48162225fcdb2d5a3ed65a07baac614ab91432fd4f57"},"source":{"id":"2103.01342","kind":"arxiv","version":3},"verdict":{"created_at":null,"id":null,"model_set":{},"one_line_summary":"","pipeline_version":null,"pith_extraction_headline":"","strongest_claim":"","weakest_assumption":""}},"verdict_id":null}}],"author_attestations":[],"timestamp_anchors":[],"storage_attestations":[],"citation_signatures":[],"replication_records":[],"corrections":[],"mirror_hints":[],"record_created":{"event_id":"sha256:311a2ead2748288628229ecb237d70e52084592c84d83a4d89f0efd9cd1dab95","target":"record","created_at":"2026-07-05T05:44:23Z","signer":{"key_id":"pith-v1-2026-05","public_key_fingerprint":"8d4b5ee74e4693bcd1df2446408b0d54","signer_id":"pith.science","signer_type":"pith_registry"},"payload":{"attestation_state":"computed","canonical_record":{"metadata":{"abstract_canon_sha256":"2ec0de1c56b5e75cbc4c2b9c860a4352e72eef2d5531270140f976e38ba140fb","cross_cats_sorted":["cs.NA","math.NA"],"license":"http://arxiv.org/licenses/nonexclusive-distrib/1.0/","primary_cat":"cs.LG","submitted_at":"2021-03-01T22:55:48Z","title_canon_sha256":"3a74fa20a67a0c1ba1f76c0dbf37819a1c64d23b5737baf77f8da7ab1fabb2a1"},"schema_version":"1.0","source":{"id":"2103.01342","kind":"arxiv","version":3}},"canonical_sha256":"fb0c278eae985979cd6a0d817ad8c369132a607adf10a06b26872554cd3ec6ca","receipt":{"algorithm":"ed25519","builder_version":"pith-number-builder-2026-05-17-v1","canonical_sha256":"fb0c278eae985979cd6a0d817ad8c369132a607adf10a06b26872554cd3ec6ca","first_computed_at":"2026-07-05T05:44:23.317476Z","key_id":"pith-v1-2026-05","kind":"pith_receipt","last_reissued_at":"2026-07-05T05:44:23.317476Z","public_key_fingerprint":"8d4b5ee74e4693bcd1df2446408b0d54","receipt_version":"0.3","signature_b64":"tOnzY+5OQTPwt5j0eOxOfKFefr+HUFh4WeEjkNnnsafUB1qT60RJsVB3MRMtaNZqtRVvuc+mvA61pELZi8vGBg==","signature_status":"signed_v1","signed_at":"2026-07-05T05:44:23.317991Z","signed_message":"canonical_sha256_bytes"},"source_id":"2103.01342","source_kind":"arxiv","source_version":3}}},"equivocations":[],"invalid_events":[],"applied_event_ids":["sha256:311a2ead2748288628229ecb237d70e52084592c84d83a4d89f0efd9cd1dab95","sha256:8b2a69ddfe2e120508dd583da944038c36666f545deaf206fde29a676b97903c"],"state_sha256":"5500c0257410f67ba02a95ca07ee61003fba90aaf49c16d043541d0e1afcc2c7"}