{"bundle_type":"pith_open_graph_bundle","bundle_version":"1.0","pith_number":"pith:2026:ERTGNUO3TRC4WN4YJ5O3QOQQJO","short_pith_number":"pith:ERTGNUO3","canonical_record":{"source":{"id":"2604.04453","kind":"arxiv","version":2},"metadata":{"license":"http://creativecommons.org/licenses/by-nc-sa/4.0/","primary_cat":"cs.CE","submitted_at":"2026-04-06T05:59:54Z","cross_cats_sorted":["cs.LG"],"title_canon_sha256":"deb0dbfa33875a239e2d499b84168c70c4873beebd01fe6c2667b735116f3f5c","abstract_canon_sha256":"290d8fdcf6cebf98b6a416a0fc32e5c320c1a002a20abf5ad2c857cfb1bd80a2"},"schema_version":"1.0"},"canonical_sha256":"246666d1db9c45cb37984f5db83a104b880ffc072934dbb0ea894158cb4961de","source":{"kind":"arxiv","id":"2604.04453","version":2},"source_aliases":[{"alias_kind":"arxiv","alias_value":"2604.04453","created_at":"2026-05-26T01:03:29Z"},{"alias_kind":"arxiv_version","alias_value":"2604.04453v2","created_at":"2026-05-26T01:03:29Z"},{"alias_kind":"doi","alias_value":"10.48550/arxiv.2604.04453","created_at":"2026-05-26T01:03:29Z"},{"alias_kind":"pith_short_12","alias_value":"ERTGNUO3TRC4","created_at":"2026-05-26T01:03:29Z"},{"alias_kind":"pith_short_16","alias_value":"ERTGNUO3TRC4WN4Y","created_at":"2026-05-26T01:03:29Z"},{"alias_kind":"pith_short_8","alias_value":"ERTGNUO3","created_at":"2026-05-26T01:03:29Z"}],"events":[{"event_type":"record_created","subject_pith_number":"pith:2026:ERTGNUO3TRC4WN4YJ5O3QOQQJO","target":"record","payload":{"canonical_record":{"source":{"id":"2604.04453","kind":"arxiv","version":2},"metadata":{"license":"http://creativecommons.org/licenses/by-nc-sa/4.0/","primary_cat":"cs.CE","submitted_at":"2026-04-06T05:59:54Z","cross_cats_sorted":["cs.LG"],"title_canon_sha256":"deb0dbfa33875a239e2d499b84168c70c4873beebd01fe6c2667b735116f3f5c","abstract_canon_sha256":"290d8fdcf6cebf98b6a416a0fc32e5c320c1a002a20abf5ad2c857cfb1bd80a2"},"schema_version":"1.0"},"canonical_sha256":"246666d1db9c45cb37984f5db83a104b880ffc072934dbb0ea894158cb4961de","receipt":{"kind":"pith_receipt","key_id":"pith-v1-2026-05","algorithm":"ed25519","signed_at":"2026-05-26T01:03:29.444061Z","signature_b64":"Pqn/PbOO2PBQEgctbopxYLII8yzDZpO1V8YGs5NIOfG9/QwJPc1zUs5Awyk/PpModeHgEb405ukT2XnZel88BQ==","signed_message":"canonical_sha256_bytes","builder_version":"pith-number-builder-2026-05-17-v1","receipt_version":"0.3","canonical_sha256":"246666d1db9c45cb37984f5db83a104b880ffc072934dbb0ea894158cb4961de","last_reissued_at":"2026-05-26T01:03:29.443275Z","signature_status":"signed_v1","first_computed_at":"2026-05-26T01:03:29.443275Z","public_key_fingerprint":"8d4b5ee74e4693bcd1df2446408b0d54"},"source_kind":"arxiv","source_id":"2604.04453","source_version":2,"attestation_state":"computed"},"signer":{"signer_id":"pith.science","signer_type":"pith_registry","key_id":"pith-v1-2026-05","public_key_fingerprint":"8d4b5ee74e4693bcd1df2446408b0d54"},"created_at":"2026-05-26T01:03:29Z","supersedes":[],"prev_event":null,"signature":{"signature_status":"signed_v1","algorithm":"ed25519","key_id":"pith-v1-2026-05","public_key_fingerprint":"8d4b5ee74e4693bcd1df2446408b0d54","signature_b64":"sCWoSHY8cK2YIetnYVtPRiBWAqZp2wdC8mJ5P8J2WcFpubgWOMM68Bfc+xMNt0LOrXHEQSsQH8EaJ9x3XgFKAg==","signed_message":"open_graph_event_sha256_bytes","signed_at":"2026-05-30T09:31:10.352344Z"},"content_sha256":"85fcc106d38d2e649be9c5e64f62294947b28047e87a1f44a282a235fd4acab4","schema_version":"1.0","event_id":"sha256:85fcc106d38d2e649be9c5e64f62294947b28047e87a1f44a282a235fd4acab4"},{"event_type":"graph_snapshot","subject_pith_number":"pith:2026:ERTGNUO3TRC4WN4YJ5O3QOQQJO","target":"graph","payload":{"graph_snapshot":{"paper":{"title":"Generative modeling of granular flow on inclined planes using conditional flow matching","license":"http://creativecommons.org/licenses/by-nc-sa/4.0/","headline":"A conditional flow matching model reconstructs interior granular velocities and stresses from sparse boundary data by training on particle simulations and enforcing consistency via a differentiable forward operator.","cross_cats":["cs.LG"],"primary_cat":"cs.CE","authors_text":"Rui Li, Teng Man, Xuyang Li, Yimin Lu","submitted_at":"2026-04-06T05:59:54Z","abstract_excerpt":"Granular flows govern many natural and industrial processes, yet their interior kinematics and mechanics remain largely unobservable, as experiments access only boundaries or free surfaces. Conventional numerical simulations are computationally expensive for fast inverse reconstruction, and deterministic models tend to collapse to over-smoothed mean predictions in ill-posed settings. This study, to the best of the authors' knowledge, presents the first conditional flow matching (CFM) framework for granular-flow reconstruction from sparse boundary observations. Trained on high-fidelity particle"},"claims":{"count":4,"items":[{"kind":"strongest_claim","text":"This study... presents the first conditional flow matching (CFM) framework for granular-flow reconstruction from sparse boundary observations... The framework accurately recovers interior flow fields from full observation to only 16% of the informative window, and it remains effective under strongly diluted spatial resolution with only 11% of data. It also outperforms a deterministic CNN baseline in the most ill-posed reconstruction regime.","source":"verdict.strongest_claim","status":"machine_extracted","claim_id":"C1","attestation":"unclaimed"},{"kind":"weakest_assumption","text":"That a model trained exclusively on high-fidelity particle-resolved discrete element simulations, when guided at inference by a differentiable forward operator, will produce physically consistent reconstructions from real-world sparse boundary observations without introducing unphysical artifacts or requiring hyperparameter tuning.","source":"verdict.weakest_assumption","status":"machine_extracted","claim_id":"C2","attestation":"unclaimed"},{"kind":"one_line_summary","text":"A conditional flow matching model trained on DEM simulations reconstructs granular flow velocity fields from as little as 11-16% sparse boundary data, outperforming deterministic CNN baselines while providing uncertainty estimates via ensemble generation.","source":"verdict.one_line_summary","status":"machine_extracted","claim_id":"C3","attestation":"unclaimed"},{"kind":"headline","text":"A conditional flow matching model reconstructs interior granular velocities and stresses from sparse boundary data by training on particle simulations and enforcing consistency via a differentiable forward operator.","source":"verdict.pith_extraction.headline","status":"machine_extracted","claim_id":"C4","attestation":"unclaimed"}],"snapshot_sha256":"7b77cf3585b707888cd0c513db99fb2b5654b933d8e1c1cd2034502928fa3f9c"},"source":{"id":"2604.04453","kind":"arxiv","version":2},"verdict":{"id":"2103a844-2640-4199-8631-fc28028aa6d8","model_set":{"reader":"grok-4.3"},"created_at":"2026-05-10T20:11:11.040351Z","strongest_claim":"This study... presents the first conditional flow matching (CFM) framework for granular-flow reconstruction from sparse boundary observations... The framework accurately recovers interior flow fields from full observation to only 16% of the informative window, and it remains effective under strongly diluted spatial resolution with only 11% of data. It also outperforms a deterministic CNN baseline in the most ill-posed reconstruction regime.","one_line_summary":"A conditional flow matching model trained on DEM simulations reconstructs granular flow velocity fields from as little as 11-16% sparse boundary data, outperforming deterministic CNN baselines while providing uncertainty estimates via ensemble generation.","pipeline_version":"pith-pipeline@v0.9.0","weakest_assumption":"That a model trained exclusively on high-fidelity particle-resolved discrete element simulations, when guided at inference by a differentiable forward operator, will produce physically consistent reconstructions from real-world sparse boundary observations without introducing unphysical artifacts or requiring hyperparameter tuning.","pith_extraction_headline":"A conditional flow matching model reconstructs interior granular velocities and stresses from sparse boundary data by training on particle simulations and enforcing consistency via a differentiable forward operator."},"integrity":{"clean":true,"summary":{"advisory":0,"critical":0,"by_detector":{},"informational":0},"endpoint":"/pith/2604.04453/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":2,"snapshot_sha256":"b007c1d19faa92bb941c922fa6f8fa8c696ada2f9a043102c403c217db62f213"},"author_claims":{"count":0,"strong_count":0,"snapshot_sha256":"258153158e38e3291e3d48162225fcdb2d5a3ed65a07baac614ab91432fd4f57"},"builder_version":"pith-number-builder-2026-05-17-v1"},"verdict_id":"2103a844-2640-4199-8631-fc28028aa6d8"},"signer":{"signer_id":"pith.science","signer_type":"pith_registry","key_id":"pith-v1-2026-05","public_key_fingerprint":"8d4b5ee74e4693bcd1df2446408b0d54"},"created_at":"2026-05-26T01:03:29Z","supersedes":[],"prev_event":null,"signature":{"signature_status":"signed_v1","algorithm":"ed25519","key_id":"pith-v1-2026-05","public_key_fingerprint":"8d4b5ee74e4693bcd1df2446408b0d54","signature_b64":"tJjQCC0TVrZUkmyLLtymy97raD5K4KAASePdE+SnfBmMOGnFGzYXSdbp87pBJlu3wRKME32iqcrOsB5ZpL7zAw==","signed_message":"open_graph_event_sha256_bytes","signed_at":"2026-05-30T09:31:10.353441Z"},"content_sha256":"b683a50048cbb97ed7216def953fe2c183188f5d82ae901272fc39875decf84f","schema_version":"1.0","event_id":"sha256:b683a50048cbb97ed7216def953fe2c183188f5d82ae901272fc39875decf84f"}],"timestamp_proofs":[],"mirror_hints":[{"mirror_type":"https","name":"Pith Resolver","base_url":"https://pith.science","bundle_url":"https://pith.science/pith/ERTGNUO3TRC4WN4YJ5O3QOQQJO/bundle.json","state_url":"https://pith.science/pith/ERTGNUO3TRC4WN4YJ5O3QOQQJO/state.json","well_known_bundle_url":"https://pith.science/.well-known/pith/ERTGNUO3TRC4WN4YJ5O3QOQQJO/bundle.json","status":"primary"}],"public_keys":[{"key_id":"pith-v1-2026-05","algorithm":"ed25519","format":"raw","public_key_b64":"stVStoiQhXFxp4s2pdzPNoqVNBMojDU/fJ2db5S3CbM=","public_key_hex":"b2d552b68890857171a78b36a5dccf368a953413288c353f7c9d9d6f94b709b3","fingerprint_sha256_b32_first128bits":"RVFV5Z2OI2J3ZUO7ERDEBCYNKS","fingerprint_sha256_hex":"8d4b5ee74e4693bcd1df2446408b0d54","rotates_at":null,"url":"https://pith.science/pith-signing-key.json","notes":"Pith uses this Ed25519 key to sign canonical record SHA-256 digests. Verify with: ed25519_verify(public_key, message=canonical_sha256_bytes, signature=base64decode(signature_b64))."}],"merge_version":"pith-open-graph-merge-v1","built_at":"2026-05-30T09:31:10Z","links":{"resolver":"https://pith.science/pith/ERTGNUO3TRC4WN4YJ5O3QOQQJO","bundle":"https://pith.science/pith/ERTGNUO3TRC4WN4YJ5O3QOQQJO/bundle.json","state":"https://pith.science/pith/ERTGNUO3TRC4WN4YJ5O3QOQQJO/state.json","well_known_bundle":"https://pith.science/.well-known/pith/ERTGNUO3TRC4WN4YJ5O3QOQQJO/bundle.json"},"state":{"state_type":"pith_open_graph_state","state_version":"1.0","pith_number":"pith:2026:ERTGNUO3TRC4WN4YJ5O3QOQQJO","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":"290d8fdcf6cebf98b6a416a0fc32e5c320c1a002a20abf5ad2c857cfb1bd80a2","cross_cats_sorted":["cs.LG"],"license":"http://creativecommons.org/licenses/by-nc-sa/4.0/","primary_cat":"cs.CE","submitted_at":"2026-04-06T05:59:54Z","title_canon_sha256":"deb0dbfa33875a239e2d499b84168c70c4873beebd01fe6c2667b735116f3f5c"},"schema_version":"1.0","source":{"id":"2604.04453","kind":"arxiv","version":2}},"source_aliases":[{"alias_kind":"arxiv","alias_value":"2604.04453","created_at":"2026-05-26T01:03:29Z"},{"alias_kind":"arxiv_version","alias_value":"2604.04453v2","created_at":"2026-05-26T01:03:29Z"},{"alias_kind":"doi","alias_value":"10.48550/arxiv.2604.04453","created_at":"2026-05-26T01:03:29Z"},{"alias_kind":"pith_short_12","alias_value":"ERTGNUO3TRC4","created_at":"2026-05-26T01:03:29Z"},{"alias_kind":"pith_short_16","alias_value":"ERTGNUO3TRC4WN4Y","created_at":"2026-05-26T01:03:29Z"},{"alias_kind":"pith_short_8","alias_value":"ERTGNUO3","created_at":"2026-05-26T01:03:29Z"}],"graph_snapshots":[{"event_id":"sha256:b683a50048cbb97ed7216def953fe2c183188f5d82ae901272fc39875decf84f","target":"graph","created_at":"2026-05-26T01:03:29Z","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":4,"items":[{"attestation":"unclaimed","claim_id":"C1","kind":"strongest_claim","source":"verdict.strongest_claim","status":"machine_extracted","text":"This study... presents the first conditional flow matching (CFM) framework for granular-flow reconstruction from sparse boundary observations... The framework accurately recovers interior flow fields from full observation to only 16% of the informative window, and it remains effective under strongly diluted spatial resolution with only 11% of data. It also outperforms a deterministic CNN baseline in the most ill-posed reconstruction regime."},{"attestation":"unclaimed","claim_id":"C2","kind":"weakest_assumption","source":"verdict.weakest_assumption","status":"machine_extracted","text":"That a model trained exclusively on high-fidelity particle-resolved discrete element simulations, when guided at inference by a differentiable forward operator, will produce physically consistent reconstructions from real-world sparse boundary observations without introducing unphysical artifacts or requiring hyperparameter tuning."},{"attestation":"unclaimed","claim_id":"C3","kind":"one_line_summary","source":"verdict.one_line_summary","status":"machine_extracted","text":"A conditional flow matching model trained on DEM simulations reconstructs granular flow velocity fields from as little as 11-16% sparse boundary data, outperforming deterministic CNN baselines while providing uncertainty estimates via ensemble generation."},{"attestation":"unclaimed","claim_id":"C4","kind":"headline","source":"verdict.pith_extraction.headline","status":"machine_extracted","text":"A conditional flow matching model reconstructs interior granular velocities and stresses from sparse boundary data by training on particle simulations and enforcing consistency via a differentiable forward operator."}],"snapshot_sha256":"7b77cf3585b707888cd0c513db99fb2b5654b933d8e1c1cd2034502928fa3f9c"},"formal_canon":{"evidence_count":2,"snapshot_sha256":"b007c1d19faa92bb941c922fa6f8fa8c696ada2f9a043102c403c217db62f213"},"integrity":{"available":true,"clean":true,"detectors_run":[],"endpoint":"/pith/2604.04453/integrity.json","findings":[],"snapshot_sha256":"c28c3603d3b5d939e8dc4c7e95fa8dfce3d595e45f758748cecf8e644a296938","summary":{"advisory":0,"by_detector":{},"critical":0,"informational":0}},"paper":{"abstract_excerpt":"Granular flows govern many natural and industrial processes, yet their interior kinematics and mechanics remain largely unobservable, as experiments access only boundaries or free surfaces. Conventional numerical simulations are computationally expensive for fast inverse reconstruction, and deterministic models tend to collapse to over-smoothed mean predictions in ill-posed settings. This study, to the best of the authors' knowledge, presents the first conditional flow matching (CFM) framework for granular-flow reconstruction from sparse boundary observations. Trained on high-fidelity particle","authors_text":"Rui Li, Teng Man, Xuyang Li, Yimin Lu","cross_cats":["cs.LG"],"headline":"A conditional flow matching model reconstructs interior granular velocities and stresses from sparse boundary data by training on particle simulations and enforcing consistency via a differentiable forward operator.","license":"http://creativecommons.org/licenses/by-nc-sa/4.0/","primary_cat":"cs.CE","submitted_at":"2026-04-06T05:59:54Z","title":"Generative modeling of granular flow on inclined planes using conditional flow matching"},"references":{"count":0,"internal_anchors":0,"resolved_work":0,"sample":[],"snapshot_sha256":"258153158e38e3291e3d48162225fcdb2d5a3ed65a07baac614ab91432fd4f57"},"source":{"id":"2604.04453","kind":"arxiv","version":2},"verdict":{"created_at":"2026-05-10T20:11:11.040351Z","id":"2103a844-2640-4199-8631-fc28028aa6d8","model_set":{"reader":"grok-4.3"},"one_line_summary":"A conditional flow matching model trained on DEM simulations reconstructs granular flow velocity fields from as little as 11-16% sparse boundary data, outperforming deterministic CNN baselines while providing uncertainty estimates via ensemble generation.","pipeline_version":"pith-pipeline@v0.9.0","pith_extraction_headline":"A conditional flow matching model reconstructs interior granular velocities and stresses from sparse boundary data by training on particle simulations and enforcing consistency via a differentiable forward operator.","strongest_claim":"This study... presents the first conditional flow matching (CFM) framework for granular-flow reconstruction from sparse boundary observations... The framework accurately recovers interior flow fields from full observation to only 16% of the informative window, and it remains effective under strongly diluted spatial resolution with only 11% of data. It also outperforms a deterministic CNN baseline in the most ill-posed reconstruction regime.","weakest_assumption":"That a model trained exclusively on high-fidelity particle-resolved discrete element simulations, when guided at inference by a differentiable forward operator, will produce physically consistent reconstructions from real-world sparse boundary observations without introducing unphysical artifacts or requiring hyperparameter tuning."}},"verdict_id":"2103a844-2640-4199-8631-fc28028aa6d8"}}],"author_attestations":[],"timestamp_anchors":[],"storage_attestations":[],"citation_signatures":[],"replication_records":[],"corrections":[],"mirror_hints":[],"record_created":{"event_id":"sha256:85fcc106d38d2e649be9c5e64f62294947b28047e87a1f44a282a235fd4acab4","target":"record","created_at":"2026-05-26T01:03:29Z","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":"290d8fdcf6cebf98b6a416a0fc32e5c320c1a002a20abf5ad2c857cfb1bd80a2","cross_cats_sorted":["cs.LG"],"license":"http://creativecommons.org/licenses/by-nc-sa/4.0/","primary_cat":"cs.CE","submitted_at":"2026-04-06T05:59:54Z","title_canon_sha256":"deb0dbfa33875a239e2d499b84168c70c4873beebd01fe6c2667b735116f3f5c"},"schema_version":"1.0","source":{"id":"2604.04453","kind":"arxiv","version":2}},"canonical_sha256":"246666d1db9c45cb37984f5db83a104b880ffc072934dbb0ea894158cb4961de","receipt":{"algorithm":"ed25519","builder_version":"pith-number-builder-2026-05-17-v1","canonical_sha256":"246666d1db9c45cb37984f5db83a104b880ffc072934dbb0ea894158cb4961de","first_computed_at":"2026-05-26T01:03:29.443275Z","key_id":"pith-v1-2026-05","kind":"pith_receipt","last_reissued_at":"2026-05-26T01:03:29.443275Z","public_key_fingerprint":"8d4b5ee74e4693bcd1df2446408b0d54","receipt_version":"0.3","signature_b64":"Pqn/PbOO2PBQEgctbopxYLII8yzDZpO1V8YGs5NIOfG9/QwJPc1zUs5Awyk/PpModeHgEb405ukT2XnZel88BQ==","signature_status":"signed_v1","signed_at":"2026-05-26T01:03:29.444061Z","signed_message":"canonical_sha256_bytes"},"source_id":"2604.04453","source_kind":"arxiv","source_version":2}}},"equivocations":[],"invalid_events":[],"applied_event_ids":["sha256:85fcc106d38d2e649be9c5e64f62294947b28047e87a1f44a282a235fd4acab4","sha256:b683a50048cbb97ed7216def953fe2c183188f5d82ae901272fc39875decf84f"],"state_sha256":"a5fe04b3e7ff050be3e6220e75cfb969f93714b90d3fccc210e2c0fd5d7df319"},"bundle_signature":{"signature_status":"signed_v1","algorithm":"ed25519","key_id":"pith-v1-2026-05","public_key_fingerprint":"8d4b5ee74e4693bcd1df2446408b0d54","signature_b64":"Mt72A7Yi/Ko7h5QNNJBTbnKXeKTW1NQ+lgyTXS9RTPRUnwsp1Se0Dk24Z3gZQ+/nsuvIptkozQIh2Iz9/qwNBQ==","signed_message":"bundle_sha256_bytes","signed_at":"2026-05-30T09:31:10.358519Z","bundle_sha256":"c24d81ce532e1831a236e36c5396bad3bd1d3e87bc310a0b40455ce4c4aad0e2"}}