{"as_of":"2026-08-19T03:33:00Z","caps":{"database_statements":6,"inbound":100,"outbound":100},"context_digest":"sha256:a5ae74d55472a3f1a7bfcb0fd3a54488477305359fff5588df6da7cf53aa5884","coverage":[{"denominator":51,"lane":"reference_resolution","note":"Typed states for the displayed outbound observations.","records_observed":51,"source":"paper_references, paper_reference_links","source_observed_at":"2026-08-01T05:18:02.343513Z","state":"measured"},{"denominator":51,"lane":"standing_notices","note":"One-hop event checks from named stored sources.","records_observed":51,"source":"scholarly_work_events, retraction_status_cache","source_observed_at":"2026-08-18T06:34:40.430872+00:00","state":"measured"},{"denominator":0,"lane":"inbound_itemization","note":"Pith citing papers itemized under the disclosed page cap.","records_observed":0,"source":"paper_references, paper_reference_links","source_observed_at":null,"state":"measured"},{"denominator":1,"lane":"external_citation_measurements","note":"A source-named dated measurement, never combined with another source.","records_observed":0,"source":"cited_works","source_observed_at":null,"state":"measured"}],"external_citation_measurements":[],"inbound":[],"links":{"evidence":"/evidence","html":"/paper/2607.22280/citation-record","integrity":"/paper/2607.22280/integrity","json":"/paper/2607.22280/citation-record.json","paper":"/paper/2607.22280"},"outbound":[{"citation":{"cited_paper":null,"cited_work":{"arxiv_id":null,"doi":null,"metadata_source":null,"pith_arxiv_id":null,"snapshot_observed_at":"2026-08-01T05:17:56.882301Z","title":"Advances in neural information processing systems , volume =","venue":null,"work_id":null,"year":null},"citing_paper":{"arxiv_id":"2607.22280","last_updated":"2026-07-27T07:37:39Z","snapshot_observed_at":"2026-08-05T16:41:49.564894Z","submitted_at":"2026-07-24T13:20:01Z","title":"Neptuna: A Comprehensive Machine Learning Framework for Benchmarking Complex Multiphase Flows","version":2},"reference_index":1,"source":"arxiv_source","source_observed_at":"2026-08-01T05:17:56.882301Z"},"links":{"citing_paper":"/paper/2607.22280"},"observation_digest":"sha256:5c482846537c7c5dfef30819c19cd47af61cab2838449fd63f0ebeb73603329d","observation_id":"34fcc21f-b2ed-40d0-984e-45e076218920","resolution":{"observed_at":"2026-08-01T05:17:56.882301Z","resolver_source":null,"status":"unresolved"},"standing_notice":{"events":[],"reason":"canonical_work_link_unavailable","source_receipts":[],"state":"unavailable"}},{"citation":{"cited_paper":{"arxiv_id":"2209.15616","last_updated":"2022-11-15T23:07:10Z","snapshot_observed_at":"2026-08-18T11:31:05.861933Z","submitted_at":"2022-09-30T17:40:05Z","title":"Towards Multi-spatiotemporal-scale Generalized PDE Modeling","version":2},"cited_work":{"arxiv_id":null,"doi":null,"metadata_source":null,"pith_arxiv_id":"2209.15616","snapshot_observed_at":"2026-08-01T05:17:56.927762Z","title":"arXiv preprint arXiv:2209.15616 , year =","venue":null,"work_id":null,"year":null},"citing_paper":{"arxiv_id":"2607.22280","last_updated":"2026-07-27T07:37:39Z","snapshot_observed_at":"2026-08-05T16:41:49.564894Z","submitted_at":"2026-07-24T13:20:01Z","title":"Neptuna: A Comprehensive Machine Learning Framework for Benchmarking Complex Multiphase Flows","version":2},"reference_index":2,"source":"arxiv_source","source_observed_at":"2026-08-01T05:17:56.927762Z"},"links":{"cited_paper":"/paper/2209.15616","citing_paper":"/paper/2607.22280"},"observation_digest":"sha256:8ddeac011b19a658fe5539d859315ecaf4df2c782423c7e873eaa0d3788babe2","observation_id":"b8f36590-f774-4418-a4fe-2a7587732918","resolution":{"observed_at":"2026-08-01T05:17:56.927762Z","resolver_source":null,"status":"unresolved"},"standing_notice":{"events":[],"reason":"canonical_work_link_unavailable","source_receipts":[],"state":"unavailable"}},{"citation":{"cited_paper":{"arxiv_id":"2307.14623","last_updated":"2023-08-25T03:17:29Z","snapshot_observed_at":"2026-08-16T15:12:57.058678Z","submitted_at":"2023-07-27T04:47:05Z","title":"BubbleML: A Multi-Physics Dataset and Benchmarks for Machine Learning","version":2},"cited_work":{"arxiv_id":null,"doi":null,"metadata_source":null,"pith_arxiv_id":"2307.14623","snapshot_observed_at":"2026-08-01T05:17:56.992278Z","title":"arXiv preprint arXiv:2307.14623 , year =","venue":null,"work_id":null,"year":null},"citing_paper":{"arxiv_id":"2607.22280","last_updated":"2026-07-27T07:37:39Z","snapshot_observed_at":"2026-08-05T16:41:49.564894Z","submitted_at":"2026-07-24T13:20:01Z","title":"Neptuna: A Comprehensive Machine Learning Framework for Benchmarking Complex Multiphase Flows","version":2},"reference_index":3,"source":"arxiv_source","source_observed_at":"2026-08-01T05:17:56.992278Z"},"links":{"cited_paper":"/paper/2307.14623","citing_paper":"/paper/2607.22280"},"observation_digest":"sha256:01b54948b4dae2c91595bdf2d081b9fc5d08902df5160683e106fbeca28d1a73","observation_id":"a7193b17-aad6-4ee2-ac54-e41a3dc3238b","resolution":{"observed_at":"2026-08-01T05:17:56.992278Z","resolver_source":null,"status":"unresolved"},"standing_notice":{"events":[],"reason":"canonical_work_link_unavailable","source_receipts":[],"state":"unavailable"}},{"citation":{"cited_paper":null,"cited_work":{"arxiv_id":null,"doi":null,"metadata_source":null,"pith_arxiv_id":null,"snapshot_observed_at":"2026-08-01T05:17:57.081468Z","title":"The Thirty-ninth Annual Conference on Neural Information Processing Systems Datasets and Benchmarks Track , year =","venue":null,"work_id":null,"year":null},"citing_paper":{"arxiv_id":"2607.22280","last_updated":"2026-07-27T07:37:39Z","snapshot_observed_at":"2026-08-05T16:41:49.564894Z","submitted_at":"2026-07-24T13:20:01Z","title":"Neptuna: A Comprehensive Machine Learning Framework for Benchmarking Complex Multiphase Flows","version":2},"reference_index":4,"source":"arxiv_source","source_observed_at":"2026-08-01T05:17:57.081468Z"},"links":{"citing_paper":"/paper/2607.22280"},"observation_digest":"sha256:05d971235575913f46ff227997afa1bd07bf5c9296a2e95e48fd6cdeed6c757c","observation_id":"1b9bdc8d-8b5a-4ea7-b67b-a1e0dbf9deb7","resolution":{"observed_at":"2026-08-01T05:17:57.081468Z","resolver_source":null,"status":"unresolved"},"standing_notice":{"events":[],"reason":"canonical_work_link_unavailable","source_receipts":[],"state":"unavailable"}},{"citation":{"cited_paper":null,"cited_work":{"arxiv_id":null,"doi":null,"metadata_source":null,"pith_arxiv_id":null,"snapshot_observed_at":"2026-08-01T05:17:57.145788Z","title":"Advances in Neural Information Processing Systems , volume =","venue":null,"work_id":null,"year":null},"citing_paper":{"arxiv_id":"2607.22280","last_updated":"2026-07-27T07:37:39Z","snapshot_observed_at":"2026-08-05T16:41:49.564894Z","submitted_at":"2026-07-24T13:20:01Z","title":"Neptuna: A Comprehensive Machine Learning Framework for Benchmarking Complex Multiphase Flows","version":2},"reference_index":5,"source":"arxiv_source","source_observed_at":"2026-08-01T05:17:57.145788Z"},"links":{"citing_paper":"/paper/2607.22280"},"observation_digest":"sha256:89ff269a3a87874aa701c2623cf7c9de0a51f262c3ac6d3bdae71537d6b3a7ba","observation_id":"f1eb483c-c883-4745-91a8-26afa5cf7608","resolution":{"observed_at":"2026-08-01T05:17:57.145788Z","resolver_source":null,"status":"unresolved"},"standing_notice":{"events":[],"reason":"canonical_work_link_unavailable","source_receipts":[],"state":"unavailable"}},{"citation":{"cited_paper":null,"cited_work":{"arxiv_id":null,"doi":null,"metadata_source":null,"pith_arxiv_id":null,"snapshot_observed_at":"2026-08-01T05:17:57.239347Z","title":"Advances in Neural Information Processing Systems , volume =","venue":null,"work_id":null,"year":null},"citing_paper":{"arxiv_id":"2607.22280","last_updated":"2026-07-27T07:37:39Z","snapshot_observed_at":"2026-08-05T16:41:49.564894Z","submitted_at":"2026-07-24T13:20:01Z","title":"Neptuna: A Comprehensive Machine Learning Framework for Benchmarking Complex Multiphase Flows","version":2},"reference_index":6,"source":"arxiv_source","source_observed_at":"2026-08-01T05:17:57.239347Z"},"links":{"citing_paper":"/paper/2607.22280"},"observation_digest":"sha256:ac0d3cbe4c57dac7e5f30ab9f042d691dcd09525abd1288a5ce7a0bf72b509b9","observation_id":"ca08a420-d764-45ae-94a6-616a3b086eb7","resolution":{"observed_at":"2026-08-01T05:17:57.239347Z","resolver_source":null,"status":"unresolved"},"standing_notice":{"events":[],"reason":"canonical_work_link_unavailable","source_receipts":[],"state":"unavailable"}},{"citation":{"cited_paper":null,"cited_work":{"arxiv_id":null,"doi":null,"metadata_source":null,"pith_arxiv_id":null,"snapshot_observed_at":"2026-08-01T05:17:57.339979Z","title":"arXiv preprint arXiv:2601.01829 , year =","venue":null,"work_id":null,"year":null},"citing_paper":{"arxiv_id":"2607.22280","last_updated":"2026-07-27T07:37:39Z","snapshot_observed_at":"2026-08-05T16:41:49.564894Z","submitted_at":"2026-07-24T13:20:01Z","title":"Neptuna: A Comprehensive Machine Learning Framework for Benchmarking Complex Multiphase Flows","version":2},"reference_index":7,"source":"arxiv_source","source_observed_at":"2026-08-01T05:17:57.339979Z"},"links":{"citing_paper":"/paper/2607.22280"},"observation_digest":"sha256:7e566278c4b65abf48c8da53d3e4c0eab606fe0ebc20c47ed865898a2793d5ef","observation_id":"af1e215c-1a79-4776-b2c3-ccc87324c825","resolution":{"observed_at":"2026-08-01T05:17:57.339979Z","resolver_source":null,"status":"unresolved"},"standing_notice":{"events":[],"reason":"canonical_work_link_unavailable","source_receipts":[],"state":"unavailable"}},{"citation":{"cited_paper":null,"cited_work":{"arxiv_id":null,"doi":null,"metadata_source":null,"pith_arxiv_id":null,"snapshot_observed_at":"2026-08-01T05:17:57.417519Z","title":"arXiv preprint arXiv:2512.18595 , year =","venue":null,"work_id":null,"year":null},"citing_paper":{"arxiv_id":"2607.22280","last_updated":"2026-07-27T07:37:39Z","snapshot_observed_at":"2026-08-05T16:41:49.564894Z","submitted_at":"2026-07-24T13:20:01Z","title":"Neptuna: A Comprehensive Machine Learning Framework for Benchmarking Complex Multiphase Flows","version":2},"reference_index":8,"source":"arxiv_source","source_observed_at":"2026-08-01T05:17:57.417519Z"},"links":{"citing_paper":"/paper/2607.22280"},"observation_digest":"sha256:b797ae2adea339327765be8fa1c8b7c69ef977d5ef4c3c0b3fad6ab0945d7a2d","observation_id":"d7e165e7-fa7b-4b82-876b-02aa73a58766","resolution":{"observed_at":"2026-08-01T05:17:57.417519Z","resolver_source":null,"status":"unresolved"},"standing_notice":{"events":[],"reason":"canonical_work_link_unavailable","source_receipts":[],"state":"unavailable"}},{"citation":{"cited_paper":null,"cited_work":{"arxiv_id":null,"doi":null,"metadata_source":null,"pith_arxiv_id":null,"snapshot_observed_at":"2026-08-01T05:17:57.497266Z","title":"Journal of Machine Learning Research , volume =","venue":null,"work_id":null,"year":null},"citing_paper":{"arxiv_id":"2607.22280","last_updated":"2026-07-27T07:37:39Z","snapshot_observed_at":"2026-08-05T16:41:49.564894Z","submitted_at":"2026-07-24T13:20:01Z","title":"Neptuna: A Comprehensive Machine Learning Framework for Benchmarking Complex Multiphase Flows","version":2},"reference_index":9,"source":"arxiv_source","source_observed_at":"2026-08-01T05:17:57.497266Z"},"links":{"citing_paper":"/paper/2607.22280"},"observation_digest":"sha256:0ff8800783d1781ac71ba3506ad51904643c427056afdc0cd77d967af1e8b2c7","observation_id":"e934ad5b-a66c-4e65-bbb3-0231dc369f83","resolution":{"observed_at":"2026-08-01T05:17:57.497266Z","resolver_source":null,"status":"unresolved"},"standing_notice":{"events":[],"reason":"canonical_work_link_unavailable","source_receipts":[],"state":"unavailable"}},{"citation":{"cited_paper":null,"cited_work":{"arxiv_id":null,"doi":null,"metadata_source":null,"pith_arxiv_id":null,"snapshot_observed_at":"2026-08-01T05:17:57.586125Z","title":"Advances in Neural Information Processing Systems , volume =","venue":null,"work_id":null,"year":null},"citing_paper":{"arxiv_id":"2607.22280","last_updated":"2026-07-27T07:37:39Z","snapshot_observed_at":"2026-08-05T16:41:49.564894Z","submitted_at":"2026-07-24T13:20:01Z","title":"Neptuna: A Comprehensive Machine Learning Framework for Benchmarking Complex Multiphase Flows","version":2},"reference_index":10,"source":"arxiv_source","source_observed_at":"2026-08-01T05:17:57.586125Z"},"links":{"citing_paper":"/paper/2607.22280"},"observation_digest":"sha256:c3e25bf1f4e0240e1a4e7c24d79fbb077971c273ae853c7c4c29505e0d3997aa","observation_id":"be0673b9-0045-4dd8-bad3-859cdec52dbb","resolution":{"observed_at":"2026-08-01T05:17:57.586125Z","resolver_source":null,"status":"unresolved"},"standing_notice":{"events":[],"reason":"canonical_work_link_unavailable","source_receipts":[],"state":"unavailable"}},{"citation":{"cited_paper":null,"cited_work":{"arxiv_id":null,"doi":null,"metadata_source":null,"pith_arxiv_id":null,"snapshot_observed_at":"2026-08-01T05:17:57.643715Z","title":"International conference on machine learning , pages =","venue":null,"work_id":null,"year":2020},"citing_paper":{"arxiv_id":"2607.22280","last_updated":"2026-07-27T07:37:39Z","snapshot_observed_at":"2026-08-05T16:41:49.564894Z","submitted_at":"2026-07-24T13:20:01Z","title":"Neptuna: A Comprehensive Machine Learning Framework for Benchmarking Complex Multiphase Flows","version":2},"reference_index":11,"source":"arxiv_source","source_observed_at":"2026-08-01T05:17:57.643715Z"},"links":{"citing_paper":"/paper/2607.22280"},"observation_digest":"sha256:e6c80d735ef6d670085b9c4657d01f20b8d89c5893ef565c753568de98ab5c77","observation_id":"e0b1f366-9ad3-48e9-b95f-81d8e634731b","resolution":{"observed_at":"2026-08-01T05:17:57.643715Z","resolver_source":null,"status":"unresolved"},"standing_notice":{"events":[],"reason":"canonical_work_link_unavailable","source_receipts":[],"state":"unavailable"}},{"citation":{"cited_paper":{"arxiv_id":"2202.03376","last_updated":"2023-03-20T07:52:57Z","snapshot_observed_at":"2026-08-16T17:23:03.217520Z","submitted_at":"2022-02-07T17:47:46Z","title":"Message Passing Neural PDE Solvers","version":3},"cited_work":{"arxiv_id":null,"doi":null,"metadata_source":null,"pith_arxiv_id":"2202.03376","snapshot_observed_at":"2026-08-01T05:17:57.703911Z","title":"arXiv preprint arXiv:2202.03376 , year =","venue":null,"work_id":null,"year":null},"citing_paper":{"arxiv_id":"2607.22280","last_updated":"2026-07-27T07:37:39Z","snapshot_observed_at":"2026-08-05T16:41:49.564894Z","submitted_at":"2026-07-24T13:20:01Z","title":"Neptuna: A Comprehensive Machine Learning Framework for Benchmarking Complex Multiphase Flows","version":2},"reference_index":12,"source":"arxiv_source","source_observed_at":"2026-08-01T05:17:57.703911Z"},"links":{"cited_paper":"/paper/2202.03376","citing_paper":"/paper/2607.22280"},"observation_digest":"sha256:672c77c69980c863828ae4bc293b5b77765402ffcdeba689e571316707751ccf","observation_id":"4982cda2-44bb-4bf0-ae97-9410108d20c8","resolution":{"observed_at":"2026-08-01T05:17:57.703911Z","resolver_source":null,"status":"unresolved"},"standing_notice":{"events":[],"reason":"canonical_work_link_unavailable","source_receipts":[],"state":"unavailable"}},{"citation":{"cited_paper":null,"cited_work":{"arxiv_id":null,"doi":null,"metadata_source":null,"pith_arxiv_id":null,"snapshot_observed_at":"2026-08-01T05:17:57.767092Z","title":"International Conference on Machine Learning , pages =","venue":null,"work_id":null,"year":2023},"citing_paper":{"arxiv_id":"2607.22280","last_updated":"2026-07-27T07:37:39Z","snapshot_observed_at":"2026-08-05T16:41:49.564894Z","submitted_at":"2026-07-24T13:20:01Z","title":"Neptuna: A Comprehensive Machine Learning Framework for Benchmarking Complex Multiphase Flows","version":2},"reference_index":13,"source":"arxiv_source","source_observed_at":"2026-08-01T05:17:57.767092Z"},"links":{"citing_paper":"/paper/2607.22280"},"observation_digest":"sha256:75aaa90705e4fd5aa4240a90a687fa02a464e3f317df0a75671dfe23de8e4140","observation_id":"9d9b9a37-9b20-4ebb-9236-501f7b30236b","resolution":{"observed_at":"2026-08-01T05:17:57.767092Z","resolver_source":null,"status":"unresolved"},"standing_notice":{"events":[],"reason":"canonical_work_link_unavailable","source_receipts":[],"state":"unavailable"}},{"citation":{"cited_paper":null,"cited_work":{"arxiv_id":null,"doi":null,"metadata_source":null,"pith_arxiv_id":null,"snapshot_observed_at":"2026-08-01T05:17:57.866335Z","title":"Advances in Neural Information Processing Systems , volume =","venue":null,"work_id":null,"year":null},"citing_paper":{"arxiv_id":"2607.22280","last_updated":"2026-07-27T07:37:39Z","snapshot_observed_at":"2026-08-05T16:41:49.564894Z","submitted_at":"2026-07-24T13:20:01Z","title":"Neptuna: A Comprehensive Machine Learning Framework for Benchmarking Complex Multiphase Flows","version":2},"reference_index":14,"source":"arxiv_source","source_observed_at":"2026-08-01T05:17:57.866335Z"},"links":{"citing_paper":"/paper/2607.22280"},"observation_digest":"sha256:8c48c58f6df87ca2585e3649fc4dd9ac147b8886819d0a234524c3fb1619f9bc","observation_id":"b320dc65-aae0-494a-b67d-9cf13beae7d1","resolution":{"observed_at":"2026-08-01T05:17:57.866335Z","resolver_source":null,"status":"unresolved"},"standing_notice":{"events":[],"reason":"canonical_work_link_unavailable","source_receipts":[],"state":"unavailable"}},{"citation":{"cited_paper":null,"cited_work":{"arxiv_id":null,"doi":null,"metadata_source":null,"pith_arxiv_id":null,"snapshot_observed_at":"2026-08-01T05:17:57.950448Z","title":"ICLR 2023 workshop on physics for machine learning , year =","venue":null,"work_id":null,"year":2023},"citing_paper":{"arxiv_id":"2607.22280","last_updated":"2026-07-27T07:37:39Z","snapshot_observed_at":"2026-08-05T16:41:49.564894Z","submitted_at":"2026-07-24T13:20:01Z","title":"Neptuna: A Comprehensive Machine Learning Framework for Benchmarking Complex Multiphase Flows","version":2},"reference_index":15,"source":"arxiv_source","source_observed_at":"2026-08-01T05:17:57.950448Z"},"links":{"citing_paper":"/paper/2607.22280"},"observation_digest":"sha256:4cd2cf53761d49914eea9d45d4f75404a5a0cc6803af0e1fb23dccee8bd995bc","observation_id":"35773564-b9d3-43b6-9a11-684c553b7a66","resolution":{"observed_at":"2026-08-01T05:17:57.950448Z","resolver_source":null,"status":"unresolved"},"standing_notice":{"events":[],"reason":"canonical_work_link_unavailable","source_receipts":[],"state":"unavailable"}},{"citation":{"cited_paper":null,"cited_work":{"arxiv_id":null,"doi":null,"metadata_source":null,"pith_arxiv_id":null,"snapshot_observed_at":"2026-08-01T05:17:58.036175Z","title":"Proceedings of the IEEE/CVF conference on computer vision and pattern recognition , pages =","venue":null,"work_id":null,"year":null},"citing_paper":{"arxiv_id":"2607.22280","last_updated":"2026-07-27T07:37:39Z","snapshot_observed_at":"2026-08-05T16:41:49.564894Z","submitted_at":"2026-07-24T13:20:01Z","title":"Neptuna: A Comprehensive Machine Learning Framework for Benchmarking Complex Multiphase Flows","version":2},"reference_index":16,"source":"arxiv_source","source_observed_at":"2026-08-01T05:17:58.036175Z"},"links":{"citing_paper":"/paper/2607.22280"},"observation_digest":"sha256:076b792d9e031fa0bc636e8dc6e7599e2de1043eec6a6d40a46fd5141b9ba058","observation_id":"96fc518f-3aa8-4a1a-bda5-ac30a374e097","resolution":{"observed_at":"2026-08-01T05:17:58.036175Z","resolver_source":null,"status":"unresolved"},"standing_notice":{"events":[],"reason":"canonical_work_link_unavailable","source_receipts":[],"state":"unavailable"}},{"citation":{"cited_paper":{"arxiv_id":"2111.13802","last_updated":"2023-03-02T12:40:23Z","snapshot_observed_at":"2026-08-17T19:37:57.442225Z","submitted_at":"2021-11-27T03:34:13Z","title":"Factorized Fourier Neural Operators","version":4},"cited_work":{"arxiv_id":null,"doi":null,"metadata_source":null,"pith_arxiv_id":"2111.13802","snapshot_observed_at":"2026-08-01T05:17:58.087005Z","title":"arXiv preprint arXiv:2111.13802 , year =","venue":null,"work_id":null,"year":null},"citing_paper":{"arxiv_id":"2607.22280","last_updated":"2026-07-27T07:37:39Z","snapshot_observed_at":"2026-08-05T16:41:49.564894Z","submitted_at":"2026-07-24T13:20:01Z","title":"Neptuna: A Comprehensive Machine Learning Framework for Benchmarking Complex Multiphase Flows","version":2},"reference_index":17,"source":"arxiv_source","source_observed_at":"2026-08-01T05:17:58.087005Z"},"links":{"cited_paper":"/paper/2111.13802","citing_paper":"/paper/2607.22280"},"observation_digest":"sha256:2998652605d247619f459e0c3e1f86fe767cc087043df4253c0ed86926827687","observation_id":"3977b05f-3351-4390-b88e-96f1eea456b6","resolution":{"observed_at":"2026-08-01T05:17:58.087005Z","resolver_source":null,"status":"unresolved"},"standing_notice":{"events":[],"reason":"canonical_work_link_unavailable","source_receipts":[],"state":"unavailable"}},{"citation":{"cited_paper":null,"cited_work":{"arxiv_id":null,"doi":null,"metadata_source":null,"pith_arxiv_id":null,"snapshot_observed_at":"2026-08-01T05:17:58.188915Z","title":"Advances in Neural Information Processing Systems , volume =","venue":null,"work_id":null,"year":null},"citing_paper":{"arxiv_id":"2607.22280","last_updated":"2026-07-27T07:37:39Z","snapshot_observed_at":"2026-08-05T16:41:49.564894Z","submitted_at":"2026-07-24T13:20:01Z","title":"Neptuna: A Comprehensive Machine Learning Framework for Benchmarking Complex Multiphase Flows","version":2},"reference_index":18,"source":"arxiv_source","source_observed_at":"2026-08-01T05:17:58.188915Z"},"links":{"citing_paper":"/paper/2607.22280"},"observation_digest":"sha256:bbf96a2d87afbf8b2b7f51eab2753ae049574361377dd2ffd385c2a3443d2994","observation_id":"8658d783-50f1-43a4-a3e7-69803fc2c1ae","resolution":{"observed_at":"2026-08-01T05:17:58.188915Z","resolver_source":null,"status":"unresolved"},"standing_notice":{"events":[],"reason":"canonical_work_link_unavailable","source_receipts":[],"state":"unavailable"}},{"citation":{"cited_paper":null,"cited_work":{"arxiv_id":null,"doi":null,"metadata_source":null,"pith_arxiv_id":null,"snapshot_observed_at":"2026-08-01T05:17:58.419361Z","title":"2022 IEEE , author =","venue":null,"work_id":null,"year":2022},"citing_paper":{"arxiv_id":"2607.22280","last_updated":"2026-07-27T07:37:39Z","snapshot_observed_at":"2026-08-05T16:41:49.564894Z","submitted_at":"2026-07-24T13:20:01Z","title":"Neptuna: A Comprehensive Machine Learning Framework for Benchmarking Complex Multiphase Flows","version":2},"reference_index":19,"source":"arxiv_source","source_observed_at":"2026-08-01T05:17:58.419361Z"},"links":{"citing_paper":"/paper/2607.22280"},"observation_digest":"sha256:03fc3bded499c304427eb7c5a8fb9834b584a7df5f24e74ed18276eb14742545","observation_id":"98f77cd6-4304-423d-a979-b4ef0b12eb56","resolution":{"observed_at":"2026-08-01T05:17:58.419361Z","resolver_source":null,"status":"unresolved"},"standing_notice":{"events":[],"reason":"canonical_work_link_unavailable","source_receipts":[],"state":"unavailable"}},{"citation":{"cited_paper":{"arxiv_id":"2403.03542","last_updated":"2024-05-07T01:57:00Z","snapshot_observed_at":"2026-08-16T14:12:23.967060Z","submitted_at":"2024-03-06T08:38:34Z","title":"DPOT: Auto-Regressive Denoising Operator Transformer for Large-Scale PDE Pre-Training","version":4},"cited_work":{"arxiv_id":null,"doi":null,"metadata_source":null,"pith_arxiv_id":"2403.03542","snapshot_observed_at":"2026-08-01T05:17:58.589675Z","title":"arXiv preprint arXiv:2403.03542 , year =","venue":null,"work_id":null,"year":null},"citing_paper":{"arxiv_id":"2607.22280","last_updated":"2026-07-27T07:37:39Z","snapshot_observed_at":"2026-08-05T16:41:49.564894Z","submitted_at":"2026-07-24T13:20:01Z","title":"Neptuna: A Comprehensive Machine Learning Framework for Benchmarking Complex Multiphase Flows","version":2},"reference_index":20,"source":"arxiv_source","source_observed_at":"2026-08-01T05:17:58.589675Z"},"links":{"cited_paper":"/paper/2403.03542","citing_paper":"/paper/2607.22280"},"observation_digest":"sha256:4dd06886eb7d76f297aec4abd2ef3922112d628633db7af2d04baf7c9bcc0d99","observation_id":"2d82fd58-3991-42e7-bd5c-d3ba235f75c6","resolution":{"observed_at":"2026-08-01T05:17:58.589675Z","resolver_source":null,"status":"unresolved"},"standing_notice":{"events":[],"reason":"canonical_work_link_unavailable","source_receipts":[],"state":"unavailable"}},{"citation":{"cited_paper":null,"cited_work":{"arxiv_id":null,"doi":null,"metadata_source":null,"pith_arxiv_id":null,"snapshot_observed_at":"2026-08-01T05:17:58.755197Z","title":"2019 , url =","venue":null,"work_id":null,"year":2019},"citing_paper":{"arxiv_id":"2607.22280","last_updated":"2026-07-27T07:37:39Z","snapshot_observed_at":"2026-08-05T16:41:49.564894Z","submitted_at":"2026-07-24T13:20:01Z","title":"Neptuna: A Comprehensive Machine Learning Framework for Benchmarking Complex Multiphase Flows","version":2},"reference_index":21,"source":"arxiv_source","source_observed_at":"2026-08-01T05:17:58.755197Z"},"links":{"citing_paper":"/paper/2607.22280"},"observation_digest":"sha256:1add8bdaba5a8db3c812ff41dd68c268d53357a01fe09f6c95873a2947ea58c8","observation_id":"6e198d9e-9b4b-4d90-b61a-71bb94724ea3","resolution":{"observed_at":"2026-08-01T05:17:58.755197Z","resolver_source":null,"status":"unresolved"},"standing_notice":{"events":[],"reason":"canonical_work_link_unavailable","source_receipts":[],"state":"unavailable"}},{"citation":{"cited_paper":null,"cited_work":{"arxiv_id":null,"doi":null,"metadata_source":null,"pith_arxiv_id":null,"snapshot_observed_at":"2026-08-01T05:17:58.937943Z","title":"2024 , eprint =","venue":null,"work_id":null,"year":2024},"citing_paper":{"arxiv_id":"2607.22280","last_updated":"2026-07-27T07:37:39Z","snapshot_observed_at":"2026-08-05T16:41:49.564894Z","submitted_at":"2026-07-24T13:20:01Z","title":"Neptuna: A Comprehensive Machine Learning Framework for Benchmarking Complex Multiphase Flows","version":2},"reference_index":22,"source":"arxiv_source","source_observed_at":"2026-08-01T05:17:58.937943Z"},"links":{"citing_paper":"/paper/2607.22280"},"observation_digest":"sha256:c660d95075a6a36265f772c2d4cb67713d8d4a7050e9344caffef69220bd9f6f","observation_id":"55300d92-c8aa-49f3-8b5c-0c43664f71bd","resolution":{"observed_at":"2026-08-01T05:17:58.937943Z","resolver_source":null,"status":"unresolved"},"standing_notice":{"events":[],"reason":"canonical_work_link_unavailable","source_receipts":[],"state":"unavailable"}},{"citation":{"cited_paper":null,"cited_work":{"arxiv_id":null,"doi":null,"metadata_source":null,"pith_arxiv_id":null,"snapshot_observed_at":"2026-08-01T05:17:59.098243Z","title":"2024 , eprint =","venue":null,"work_id":null,"year":2024},"citing_paper":{"arxiv_id":"2607.22280","last_updated":"2026-07-27T07:37:39Z","snapshot_observed_at":"2026-08-05T16:41:49.564894Z","submitted_at":"2026-07-24T13:20:01Z","title":"Neptuna: A Comprehensive Machine Learning Framework for Benchmarking Complex Multiphase Flows","version":2},"reference_index":23,"source":"arxiv_source","source_observed_at":"2026-08-01T05:17:59.098243Z"},"links":{"citing_paper":"/paper/2607.22280"},"observation_digest":"sha256:f663dd887e00bbd69b44765142d48ec0d6232f5718e0963ed151250111cb40bf","observation_id":"8e28436e-59db-4f72-97df-6cb5230a4517","resolution":{"observed_at":"2026-08-01T05:17:59.098243Z","resolver_source":null,"status":"unresolved"},"standing_notice":{"events":[],"reason":"canonical_work_link_unavailable","source_receipts":[],"state":"unavailable"}},{"citation":{"cited_paper":null,"cited_work":{"arxiv_id":null,"doi":null,"metadata_source":null,"pith_arxiv_id":null,"snapshot_observed_at":"2026-08-01T05:17:59.216785Z","title":"2022 , eprint =","venue":null,"work_id":null,"year":2022},"citing_paper":{"arxiv_id":"2607.22280","last_updated":"2026-07-27T07:37:39Z","snapshot_observed_at":"2026-08-05T16:41:49.564894Z","submitted_at":"2026-07-24T13:20:01Z","title":"Neptuna: A Comprehensive Machine Learning Framework for Benchmarking Complex Multiphase Flows","version":2},"reference_index":24,"source":"arxiv_source","source_observed_at":"2026-08-01T05:17:59.216785Z"},"links":{"citing_paper":"/paper/2607.22280"},"observation_digest":"sha256:b94fda72008aeac16fad816e35a378896fc9120ed9563e1bd48e694e1aef208c","observation_id":"fd8bbd06-2e8b-4a17-8da4-8378161f0bee","resolution":{"observed_at":"2026-08-01T05:17:59.216785Z","resolver_source":null,"status":"unresolved"},"standing_notice":{"events":[],"reason":"canonical_work_link_unavailable","source_receipts":[],"state":"unavailable"}},{"citation":{"cited_paper":{"arxiv_id":"2006.13846","last_updated":"2020-06-29T23:15:28Z","snapshot_observed_at":"2026-08-14T18:15:58.065376Z","submitted_at":"2020-06-24T16:19:30Z","title":"Understanding SSIM","version":2},"cited_work":{"arxiv_id":null,"doi":null,"metadata_source":null,"pith_arxiv_id":"2006.13846","snapshot_observed_at":"2026-08-01T05:17:59.359127Z","title":"arXiv preprint arXiv:2006.13846 , year =","venue":null,"work_id":null,"year":2006},"citing_paper":{"arxiv_id":"2607.22280","last_updated":"2026-07-27T07:37:39Z","snapshot_observed_at":"2026-08-05T16:41:49.564894Z","submitted_at":"2026-07-24T13:20:01Z","title":"Neptuna: A Comprehensive Machine Learning Framework for Benchmarking Complex Multiphase Flows","version":2},"reference_index":25,"source":"arxiv_source","source_observed_at":"2026-08-01T05:17:59.359127Z"},"links":{"cited_paper":"/paper/2006.13846","citing_paper":"/paper/2607.22280"},"observation_digest":"sha256:3a0ba23e82db687de30208bcc5bfc33c23be60ef52b5c156701fc85914fd354e","observation_id":"889fa058-1276-4809-9284-4f17b080cbe5","resolution":{"observed_at":"2026-08-01T05:17:59.359127Z","resolver_source":null,"status":"unresolved"},"standing_notice":{"events":[],"reason":"canonical_work_link_unavailable","source_receipts":[],"state":"unavailable"}},{"citation":{"cited_paper":null,"cited_work":{"arxiv_id":null,"doi":null,"metadata_source":null,"pith_arxiv_id":null,"snapshot_observed_at":"2026-08-01T05:17:59.553070Z","title":"IEEE Access , volume =","venue":null,"work_id":null,"year":2021},"citing_paper":{"arxiv_id":"2607.22280","last_updated":"2026-07-27T07:37:39Z","snapshot_observed_at":"2026-08-05T16:41:49.564894Z","submitted_at":"2026-07-24T13:20:01Z","title":"Neptuna: A Comprehensive Machine Learning Framework for Benchmarking Complex Multiphase Flows","version":2},"reference_index":26,"source":"arxiv_source","source_observed_at":"2026-08-01T05:17:59.553070Z"},"links":{"citing_paper":"/paper/2607.22280"},"observation_digest":"sha256:470eb77272a412f8202336158417df8443239186ac1dfdc10428137336a55820","observation_id":"cf0da836-a86d-45ca-b65d-d354db7b0567","resolution":{"observed_at":"2026-08-01T05:17:59.553070Z","resolver_source":null,"status":"unresolved"},"standing_notice":{"events":[],"reason":"canonical_work_link_unavailable","source_receipts":[],"state":"unavailable"}},{"citation":{"cited_paper":{"arxiv_id":"1912.12355","last_updated":"2019-12-27T22:23:16Z","snapshot_observed_at":"2026-08-13T11:08:29.307101Z","submitted_at":"2019-12-27T22:23:16Z","title":"SoftAdapt: Techniques for Adaptive Loss Weighting of Neural Networks with Multi-Part Loss Functions","version":1},"cited_work":{"arxiv_id":null,"doi":null,"metadata_source":null,"pith_arxiv_id":"1912.12355","snapshot_observed_at":"2026-08-01T05:17:59.686836Z","title":"arXiv preprint arXiv:1912.12355 , year =","venue":null,"work_id":null,"year":1912},"citing_paper":{"arxiv_id":"2607.22280","last_updated":"2026-07-27T07:37:39Z","snapshot_observed_at":"2026-08-05T16:41:49.564894Z","submitted_at":"2026-07-24T13:20:01Z","title":"Neptuna: A Comprehensive Machine Learning Framework for Benchmarking Complex Multiphase Flows","version":2},"reference_index":27,"source":"arxiv_source","source_observed_at":"2026-08-01T05:17:59.686836Z"},"links":{"cited_paper":"/paper/1912.12355","citing_paper":"/paper/2607.22280"},"observation_digest":"sha256:e932d96b634666d1d9771c78fd4eb032056ecb390175f8e5c750dc27c2fd9350","observation_id":"b01fd583-df90-4add-9240-5e1b288b14c4","resolution":{"observed_at":"2026-08-01T05:17:59.686836Z","resolver_source":null,"status":"unresolved"},"standing_notice":{"events":[],"reason":"canonical_work_link_unavailable","source_receipts":[],"state":"unavailable"}},{"citation":{"cited_paper":{"arxiv_id":"1711.02257","last_updated":"2018-06-12T06:45:49Z","snapshot_observed_at":"2026-08-14T20:16:18.909353Z","submitted_at":"2017-11-07T02:08:12Z","title":"GradNorm: Gradient Normalization for Adaptive Loss Balancing in Deep Multitask Networks","version":4},"cited_work":{"arxiv_id":null,"doi":null,"metadata_source":null,"pith_arxiv_id":"1711.02257","snapshot_observed_at":"2026-08-01T05:17:59.857915Z","title":"URL http://arxiv","venue":null,"work_id":null,"year":2018},"citing_paper":{"arxiv_id":"2607.22280","last_updated":"2026-07-27T07:37:39Z","snapshot_observed_at":"2026-08-05T16:41:49.564894Z","submitted_at":"2026-07-24T13:20:01Z","title":"Neptuna: A Comprehensive Machine Learning Framework for Benchmarking Complex Multiphase Flows","version":2},"reference_index":28,"source":"arxiv_source","source_observed_at":"2026-08-01T05:17:59.857915Z"},"links":{"cited_paper":"/paper/1711.02257","citing_paper":"/paper/2607.22280"},"observation_digest":"sha256:1dfd3a04539d0778b7780a39083897e0a1488ddea6497ac25b5ddcc9f1bdc188","observation_id":"387a0bd9-8836-49d1-a966-3803ac2ff5a9","resolution":{"observed_at":"2026-08-01T05:17:59.857915Z","resolver_source":null,"status":"unresolved"},"standing_notice":{"events":[],"reason":"canonical_work_link_unavailable","source_receipts":[],"state":"unavailable"}},{"citation":{"cited_paper":null,"cited_work":{"arxiv_id":null,"doi":null,"metadata_source":null,"pith_arxiv_id":null,"snapshot_observed_at":"2026-08-01T05:18:00.016594Z","title":"Computer Methods in Applied Mechanics and Engineering , volume =","venue":null,"work_id":null,"year":2025},"citing_paper":{"arxiv_id":"2607.22280","last_updated":"2026-07-27T07:37:39Z","snapshot_observed_at":"2026-08-05T16:41:49.564894Z","submitted_at":"2026-07-24T13:20:01Z","title":"Neptuna: A Comprehensive Machine Learning Framework for Benchmarking Complex Multiphase Flows","version":2},"reference_index":29,"source":"arxiv_source","source_observed_at":"2026-08-01T05:18:00.016594Z"},"links":{"citing_paper":"/paper/2607.22280"},"observation_digest":"sha256:c87d8149e51977d4bcf2dd7275f45a885eabfcfee046d45a90a1d2a157d62f56","observation_id":"59066735-df54-46af-ba33-93f74663344d","resolution":{"observed_at":"2026-08-01T05:18:00.016594Z","resolver_source":null,"status":"unresolved"},"standing_notice":{"events":[],"reason":"canonical_work_link_unavailable","source_receipts":[],"state":"unavailable"}},{"citation":{"cited_paper":null,"cited_work":{"arxiv_id":null,"doi":null,"metadata_source":null,"pith_arxiv_id":null,"snapshot_observed_at":"2026-08-01T05:18:00.140268Z","title":null,"venue":null,"work_id":null,"year":null},"citing_paper":{"arxiv_id":"2607.22280","last_updated":"2026-07-27T07:37:39Z","snapshot_observed_at":"2026-08-05T16:41:49.564894Z","submitted_at":"2026-07-24T13:20:01Z","title":"Neptuna: A Comprehensive Machine Learning Framework for Benchmarking Complex Multiphase Flows","version":2},"reference_index":30,"source":"arxiv_source","source_observed_at":"2026-08-01T05:18:00.140268Z"},"links":{"citing_paper":"/paper/2607.22280"},"observation_digest":"sha256:6f603f82f4a8c9d690881f87e213841e201479a4f054214e31b75d89ab063745","observation_id":"bb4a0ec4-ed59-4443-b908-e11ec5a6560a","resolution":{"observed_at":"2026-08-01T05:18:00.140268Z","resolver_source":null,"status":"unresolved"},"standing_notice":{"events":[],"reason":"canonical_work_link_unavailable","source_receipts":[],"state":"unavailable"}},{"citation":{"cited_paper":{"arxiv_id":"2111.03794","last_updated":"2023-07-29T07:58:37Z","snapshot_observed_at":"2026-08-18T13:35:47.568471Z","submitted_at":"2021-11-06T03:41:34Z","title":"Physics-Informed Neural Operator for Learning Partial Differential Equations","version":4},"cited_work":{"arxiv_id":null,"doi":null,"metadata_source":null,"pith_arxiv_id":"2111.03794","snapshot_observed_at":"2026-08-01T05:18:00.291690Z","title":"arXiv , author =","venue":null,"work_id":null,"year":null},"citing_paper":{"arxiv_id":"2607.22280","last_updated":"2026-07-27T07:37:39Z","snapshot_observed_at":"2026-08-05T16:41:49.564894Z","submitted_at":"2026-07-24T13:20:01Z","title":"Neptuna: A Comprehensive Machine Learning Framework for Benchmarking Complex Multiphase Flows","version":2},"reference_index":31,"source":"arxiv_source","source_observed_at":"2026-08-01T05:18:00.291690Z"},"links":{"cited_paper":"/paper/2111.03794","citing_paper":"/paper/2607.22280"},"observation_digest":"sha256:a12bd2a7d14525f293299d03c4ac87d512eda02a67c753268669e3b5dc5a7a20","observation_id":"1cf982a6-ac44-46ca-af75-d682f309bd34","resolution":{"observed_at":"2026-08-01T05:18:00.291690Z","resolver_source":null,"status":"unresolved"},"standing_notice":{"events":[],"reason":"canonical_work_link_unavailable","source_receipts":[],"state":"unavailable"}},{"citation":{"cited_paper":{"arxiv_id":"2402.09084","last_updated":"2024-02-14T10:57:29Z","snapshot_observed_at":"2026-08-18T02:35:18.303189Z","submitted_at":"2024-02-14T10:57:29Z","title":"Sobolev Training for Operator Learning","version":1},"cited_work":{"arxiv_id":null,"doi":null,"metadata_source":null,"pith_arxiv_id":"2402.09084","snapshot_observed_at":"2026-08-01T05:18:00.454380Z","title":"arXiv preprint arXiv:2402.09084 , year =","venue":null,"work_id":null,"year":null},"citing_paper":{"arxiv_id":"2607.22280","last_updated":"2026-07-27T07:37:39Z","snapshot_observed_at":"2026-08-05T16:41:49.564894Z","submitted_at":"2026-07-24T13:20:01Z","title":"Neptuna: A Comprehensive Machine Learning Framework for Benchmarking Complex Multiphase Flows","version":2},"reference_index":32,"source":"arxiv_source","source_observed_at":"2026-08-01T05:18:00.454380Z"},"links":{"cited_paper":"/paper/2402.09084","citing_paper":"/paper/2607.22280"},"observation_digest":"sha256:641f2b9b917722e0034201801d401b7c912576282751face26e2fc751c5700c4","observation_id":"f02075a6-365f-42bf-bab0-cd081235f058","resolution":{"observed_at":"2026-08-01T05:18:00.454380Z","resolver_source":null,"status":"unresolved"},"standing_notice":{"events":[],"reason":"canonical_work_link_unavailable","source_receipts":[],"state":"unavailable"}},{"citation":{"cited_paper":null,"cited_work":{"arxiv_id":null,"doi":null,"metadata_source":null,"pith_arxiv_id":null,"snapshot_observed_at":"2026-08-01T05:18:00.569769Z","title":"Manufacturing Letters , volume =","venue":null,"work_id":null,"year":2025},"citing_paper":{"arxiv_id":"2607.22280","last_updated":"2026-07-27T07:37:39Z","snapshot_observed_at":"2026-08-05T16:41:49.564894Z","submitted_at":"2026-07-24T13:20:01Z","title":"Neptuna: A Comprehensive Machine Learning Framework for Benchmarking Complex Multiphase Flows","version":2},"reference_index":33,"source":"arxiv_source","source_observed_at":"2026-08-01T05:18:00.569769Z"},"links":{"citing_paper":"/paper/2607.22280"},"observation_digest":"sha256:2a7f2b73b1695eaed9e4e0db489ad0da4911e14311aa62b61e4934272bd88491","observation_id":"5fc4d88b-4cee-44af-b5d0-b37f2b2bf133","resolution":{"observed_at":"2026-08-01T05:18:00.569769Z","resolver_source":null,"status":"unresolved"},"standing_notice":{"events":[],"reason":"canonical_work_link_unavailable","source_receipts":[],"state":"unavailable"}},{"citation":{"cited_paper":null,"cited_work":{"arxiv_id":null,"doi":null,"metadata_source":null,"pith_arxiv_id":null,"snapshot_observed_at":"2026-08-01T05:18:00.727279Z","title":"Journal of Computational Physics , volume =","venue":null,"work_id":null,"year":2023},"citing_paper":{"arxiv_id":"2607.22280","last_updated":"2026-07-27T07:37:39Z","snapshot_observed_at":"2026-08-05T16:41:49.564894Z","submitted_at":"2026-07-24T13:20:01Z","title":"Neptuna: A Comprehensive Machine Learning Framework for Benchmarking Complex Multiphase Flows","version":2},"reference_index":34,"source":"arxiv_source","source_observed_at":"2026-08-01T05:18:00.727279Z"},"links":{"citing_paper":"/paper/2607.22280"},"observation_digest":"sha256:7b4beae69c00543a467e79eccd8b58d20e0835c059cccf49d90f65eff1602b05","observation_id":"8e85a24d-89c3-4430-b95a-9de1bb34d4b1","resolution":{"observed_at":"2026-08-01T05:18:00.727279Z","resolver_source":null,"status":"unresolved"},"standing_notice":{"events":[],"reason":"canonical_work_link_unavailable","source_receipts":[],"state":"unavailable"}},{"citation":{"cited_paper":null,"cited_work":{"arxiv_id":null,"doi":null,"metadata_source":null,"pith_arxiv_id":null,"snapshot_observed_at":"2026-08-01T05:18:00.858675Z","title":"Computer Physics Communications , volume =","venue":null,"work_id":null,"year":2022},"citing_paper":{"arxiv_id":"2607.22280","last_updated":"2026-07-27T07:37:39Z","snapshot_observed_at":"2026-08-05T16:41:49.564894Z","submitted_at":"2026-07-24T13:20:01Z","title":"Neptuna: A Comprehensive Machine Learning Framework for Benchmarking Complex Multiphase Flows","version":2},"reference_index":35,"source":"arxiv_source","source_observed_at":"2026-08-01T05:18:00.858675Z"},"links":{"citing_paper":"/paper/2607.22280"},"observation_digest":"sha256:262605d2e1568699bebd22d606ed347991ba61ee9e5150925ef5aaa220243263","observation_id":"7bbc71d4-1638-49ca-8a26-0a2da4b6e596","resolution":{"observed_at":"2026-08-01T05:18:00.858675Z","resolver_source":null,"status":"unresolved"},"standing_notice":{"events":[],"reason":"canonical_work_link_unavailable","source_receipts":[],"state":"unavailable"}},{"citation":{"cited_paper":null,"cited_work":{"arxiv_id":null,"doi":null,"metadata_source":null,"pith_arxiv_id":null,"snapshot_observed_at":"2026-08-01T05:18:00.918517Z","title":"(No Title) , year =","venue":null,"work_id":null,"year":null},"citing_paper":{"arxiv_id":"2607.22280","last_updated":"2026-07-27T07:37:39Z","snapshot_observed_at":"2026-08-05T16:41:49.564894Z","submitted_at":"2026-07-24T13:20:01Z","title":"Neptuna: A Comprehensive Machine Learning Framework for Benchmarking Complex Multiphase Flows","version":2},"reference_index":36,"source":"arxiv_source","source_observed_at":"2026-08-01T05:18:00.918517Z"},"links":{"citing_paper":"/paper/2607.22280"},"observation_digest":"sha256:56c8694fb45ea197d81c08c5b0f6e993c49f5cb7ac76904d238dfa55eecbc53c","observation_id":"016931cd-fc03-472a-9657-ed80c551d700","resolution":{"observed_at":"2026-08-01T05:18:00.918517Z","resolver_source":null,"status":"unresolved"},"standing_notice":{"events":[],"reason":"canonical_work_link_unavailable","source_receipts":[],"state":"unavailable"}},{"citation":{"cited_paper":null,"cited_work":{"arxiv_id":null,"doi":null,"metadata_source":null,"pith_arxiv_id":null,"snapshot_observed_at":"2026-08-01T05:18:01.006677Z","title":"Physics of Fluids , volume =","venue":null,"work_id":null,"year":2016},"citing_paper":{"arxiv_id":"2607.22280","last_updated":"2026-07-27T07:37:39Z","snapshot_observed_at":"2026-08-05T16:41:49.564894Z","submitted_at":"2026-07-24T13:20:01Z","title":"Neptuna: A Comprehensive Machine Learning Framework for Benchmarking Complex Multiphase Flows","version":2},"reference_index":37,"source":"arxiv_source","source_observed_at":"2026-08-01T05:18:01.006677Z"},"links":{"citing_paper":"/paper/2607.22280"},"observation_digest":"sha256:bc3182c54695270e90dff3261d4e15a144cf25f42ad351ffd99c6aae85b45be4","observation_id":"3edc468f-7ab1-4a9b-af32-271d06cc6bd2","resolution":{"observed_at":"2026-08-01T05:18:01.006677Z","resolver_source":null,"status":"unresolved"},"standing_notice":{"events":[],"reason":"canonical_work_link_unavailable","source_receipts":[],"state":"unavailable"}},{"citation":{"cited_paper":null,"cited_work":{"arxiv_id":null,"doi":null,"metadata_source":null,"pith_arxiv_id":null,"snapshot_observed_at":"2026-08-01T05:18:01.070498Z","title":"Physics of fluids , volume =","venue":null,"work_id":null,"year":2008},"citing_paper":{"arxiv_id":"2607.22280","last_updated":"2026-07-27T07:37:39Z","snapshot_observed_at":"2026-08-05T16:41:49.564894Z","submitted_at":"2026-07-24T13:20:01Z","title":"Neptuna: A Comprehensive Machine Learning Framework for Benchmarking Complex Multiphase Flows","version":2},"reference_index":38,"source":"arxiv_source","source_observed_at":"2026-08-01T05:18:01.070498Z"},"links":{"citing_paper":"/paper/2607.22280"},"observation_digest":"sha256:391909ace731c35c7701531536fbb454bb5df53a797a81c03010b75a82e130d0","observation_id":"196b9cf9-de90-4b2c-b579-b27007b232b7","resolution":{"observed_at":"2026-08-01T05:18:01.070498Z","resolver_source":null,"status":"unresolved"},"standing_notice":{"events":[],"reason":"canonical_work_link_unavailable","source_receipts":[],"state":"unavailable"}},{"citation":{"cited_paper":null,"cited_work":{"arxiv_id":null,"doi":null,"metadata_source":null,"pith_arxiv_id":null,"snapshot_observed_at":"2026-08-01T05:18:01.239891Z","title":"11th International Symposium on Turbulence and Shear Flow Phenomena, TSFP 2019 , year =","venue":null,"work_id":null,"year":2019},"citing_paper":{"arxiv_id":"2607.22280","last_updated":"2026-07-27T07:37:39Z","snapshot_observed_at":"2026-08-05T16:41:49.564894Z","submitted_at":"2026-07-24T13:20:01Z","title":"Neptuna: A Comprehensive Machine Learning Framework for Benchmarking Complex Multiphase Flows","version":2},"reference_index":39,"source":"arxiv_source","source_observed_at":"2026-08-01T05:18:01.239891Z"},"links":{"citing_paper":"/paper/2607.22280"},"observation_digest":"sha256:943d8771c5413a2229118b955176974a2a5f599fcee2f2857933397c9374d536","observation_id":"32bfccc2-bb1a-4480-bcb5-9c67294cc713","resolution":{"observed_at":"2026-08-01T05:18:01.239891Z","resolver_source":null,"status":"unresolved"},"standing_notice":{"events":[],"reason":"canonical_work_link_unavailable","source_receipts":[],"state":"unavailable"}},{"citation":{"cited_paper":null,"cited_work":{"arxiv_id":null,"doi":null,"metadata_source":null,"pith_arxiv_id":null,"snapshot_observed_at":"2026-08-01T05:18:01.337501Z","title":"Journal of Fluid Mechanics , volume =","venue":null,"work_id":null,"year":2018},"citing_paper":{"arxiv_id":"2607.22280","last_updated":"2026-07-27T07:37:39Z","snapshot_observed_at":"2026-08-05T16:41:49.564894Z","submitted_at":"2026-07-24T13:20:01Z","title":"Neptuna: A Comprehensive Machine Learning Framework for Benchmarking Complex Multiphase Flows","version":2},"reference_index":40,"source":"arxiv_source","source_observed_at":"2026-08-01T05:18:01.337501Z"},"links":{"citing_paper":"/paper/2607.22280"},"observation_digest":"sha256:b443a16b1a3d6051828c64ba392e5659fdc32445f3d0743eec15e58c98b2ba08","observation_id":"dad028dc-f814-406e-a700-6de1b0ee8fea","resolution":{"observed_at":"2026-08-01T05:18:01.337501Z","resolver_source":null,"status":"unresolved"},"standing_notice":{"events":[],"reason":"canonical_work_link_unavailable","source_receipts":[],"state":"unavailable"}},{"citation":{"cited_paper":null,"cited_work":{"arxiv_id":null,"doi":null,"metadata_source":null,"pith_arxiv_id":null,"snapshot_observed_at":"2026-08-01T05:18:01.414151Z","title":"Journal of Computational Physics , volume =","venue":null,"work_id":null,"year":2006},"citing_paper":{"arxiv_id":"2607.22280","last_updated":"2026-07-27T07:37:39Z","snapshot_observed_at":"2026-08-05T16:41:49.564894Z","submitted_at":"2026-07-24T13:20:01Z","title":"Neptuna: A Comprehensive Machine Learning Framework for Benchmarking Complex Multiphase Flows","version":2},"reference_index":41,"source":"arxiv_source","source_observed_at":"2026-08-01T05:18:01.414151Z"},"links":{"citing_paper":"/paper/2607.22280"},"observation_digest":"sha256:2a575bb13830b653b81cec54b07138c78b787ffbeac61c2e87f863ae1dfac2bd","observation_id":"14891344-a8c2-448a-93b5-b7bf1a30d7b2","resolution":{"observed_at":"2026-08-01T05:18:01.414151Z","resolver_source":null,"status":"unresolved"},"standing_notice":{"events":[],"reason":"canonical_work_link_unavailable","source_receipts":[],"state":"unavailable"}},{"citation":{"cited_paper":null,"cited_work":{"arxiv_id":null,"doi":null,"metadata_source":null,"pith_arxiv_id":null,"snapshot_observed_at":"2026-08-01T05:18:01.516178Z","title":"Journal of Fluid Mechanics , volume =","venue":null,"work_id":null,"year":2020},"citing_paper":{"arxiv_id":"2607.22280","last_updated":"2026-07-27T07:37:39Z","snapshot_observed_at":"2026-08-05T16:41:49.564894Z","submitted_at":"2026-07-24T13:20:01Z","title":"Neptuna: A Comprehensive Machine Learning Framework for Benchmarking Complex Multiphase Flows","version":2},"reference_index":42,"source":"arxiv_source","source_observed_at":"2026-08-01T05:18:01.516178Z"},"links":{"citing_paper":"/paper/2607.22280"},"observation_digest":"sha256:67e0860ef1d626ba85edc23e75437b34363eaf147d6063643167c510b05db443","observation_id":"c6dacef1-b428-478c-a6da-9b57e54f2220","resolution":{"observed_at":"2026-08-01T05:18:01.516178Z","resolver_source":null,"status":"unresolved"},"standing_notice":{"events":[],"reason":"canonical_work_link_unavailable","source_receipts":[],"state":"unavailable"}},{"citation":{"cited_paper":null,"cited_work":{"arxiv_id":null,"doi":null,"metadata_source":null,"pith_arxiv_id":null,"snapshot_observed_at":"2026-08-01T05:18:01.628192Z","title":"2012 , publisher =","venue":null,"work_id":null,"year":2012},"citing_paper":{"arxiv_id":"2607.22280","last_updated":"2026-07-27T07:37:39Z","snapshot_observed_at":"2026-08-05T16:41:49.564894Z","submitted_at":"2026-07-24T13:20:01Z","title":"Neptuna: A Comprehensive Machine Learning Framework for Benchmarking Complex Multiphase Flows","version":2},"reference_index":43,"source":"arxiv_source","source_observed_at":"2026-08-01T05:18:01.628192Z"},"links":{"citing_paper":"/paper/2607.22280"},"observation_digest":"sha256:5c15c43f8fc44cbf388a4bc75c9aece0fe091a0722eb504c7a8530c760c24663","observation_id":"2c550a2a-7000-48c4-ad60-e01e069bece7","resolution":{"observed_at":"2026-08-01T05:18:01.628192Z","resolver_source":null,"status":"unresolved"},"standing_notice":{"events":[],"reason":"canonical_work_link_unavailable","source_receipts":[],"state":"unavailable"}},{"citation":{"cited_paper":null,"cited_work":{"arxiv_id":null,"doi":null,"metadata_source":null,"pith_arxiv_id":null,"snapshot_observed_at":"2026-08-01T05:18:01.713413Z","title":"Marine environmental research , volume =","venue":null,"work_id":null,"year":2000},"citing_paper":{"arxiv_id":"2607.22280","last_updated":"2026-07-27T07:37:39Z","snapshot_observed_at":"2026-08-05T16:41:49.564894Z","submitted_at":"2026-07-24T13:20:01Z","title":"Neptuna: A Comprehensive Machine Learning Framework for Benchmarking Complex Multiphase Flows","version":2},"reference_index":44,"source":"arxiv_source","source_observed_at":"2026-08-01T05:18:01.713413Z"},"links":{"citing_paper":"/paper/2607.22280"},"observation_digest":"sha256:00986955d680fe912e0c8f7c90e10155570315347c6e631877add066d1c977bc","observation_id":"bee437e9-2476-43cc-9137-02be561a7c65","resolution":{"observed_at":"2026-08-01T05:18:01.713413Z","resolver_source":null,"status":"unresolved"},"standing_notice":{"events":[],"reason":"canonical_work_link_unavailable","source_receipts":[],"state":"unavailable"}},{"citation":{"cited_paper":null,"cited_work":{"arxiv_id":null,"doi":null,"metadata_source":null,"pith_arxiv_id":null,"snapshot_observed_at":"2026-08-01T05:18:01.787992Z","title":"2017 , publisher =","venue":null,"work_id":null,"year":2017},"citing_paper":{"arxiv_id":"2607.22280","last_updated":"2026-07-27T07:37:39Z","snapshot_observed_at":"2026-08-05T16:41:49.564894Z","submitted_at":"2026-07-24T13:20:01Z","title":"Neptuna: A Comprehensive Machine Learning Framework for Benchmarking Complex Multiphase Flows","version":2},"reference_index":45,"source":"arxiv_source","source_observed_at":"2026-08-01T05:18:01.787992Z"},"links":{"citing_paper":"/paper/2607.22280"},"observation_digest":"sha256:19fc3038ec2cc240d164f94e559b93511bf16c9407a239f12573f73b33f42fd3","observation_id":"d54adc48-b694-4add-84de-de6a9622d64d","resolution":{"observed_at":"2026-08-01T05:18:01.787992Z","resolver_source":null,"status":"unresolved"},"standing_notice":{"events":[],"reason":"canonical_work_link_unavailable","source_receipts":[],"state":"unavailable"}},{"citation":{"cited_paper":{"arxiv_id":"1607.06450","last_updated":"2016-07-21T19:57:52Z","snapshot_observed_at":"2026-08-15T04:53:45.483331Z","submitted_at":"2016-07-21T19:57:52Z","title":"Layer Normalization","version":1},"cited_work":{"arxiv_id":null,"doi":null,"metadata_source":null,"pith_arxiv_id":"1607.06450","snapshot_observed_at":"2026-08-01T05:18:01.936546Z","title":"arXiv preprint arXiv:1607.06450 , year =","venue":null,"work_id":null,"year":null},"citing_paper":{"arxiv_id":"2607.22280","last_updated":"2026-07-27T07:37:39Z","snapshot_observed_at":"2026-08-05T16:41:49.564894Z","submitted_at":"2026-07-24T13:20:01Z","title":"Neptuna: A Comprehensive Machine Learning Framework for Benchmarking Complex Multiphase Flows","version":2},"reference_index":46,"source":"arxiv_source","source_observed_at":"2026-08-01T05:18:01.936546Z"},"links":{"cited_paper":"/paper/1607.06450","citing_paper":"/paper/2607.22280"},"observation_digest":"sha256:e10674285a772d63a7c940191045ba8528e0062fa4fe181933abb77ecb0c27cc","observation_id":"d92aeb70-ca3f-441e-8699-a9ca5debdf65","resolution":{"observed_at":"2026-08-01T05:18:01.936546Z","resolver_source":null,"status":"unresolved"},"standing_notice":{"events":[],"reason":"canonical_work_link_unavailable","source_receipts":[],"state":"unavailable"}},{"citation":{"cited_paper":null,"cited_work":{"arxiv_id":null,"doi":null,"metadata_source":null,"pith_arxiv_id":null,"snapshot_observed_at":"2026-08-01T05:18:02.025258Z","title":"In Proceedings of the AAAI Conference on Artificial Intelligence , year =","venue":null,"work_id":null,"year":null},"citing_paper":{"arxiv_id":"2607.22280","last_updated":"2026-07-27T07:37:39Z","snapshot_observed_at":"2026-08-05T16:41:49.564894Z","submitted_at":"2026-07-24T13:20:01Z","title":"Neptuna: A Comprehensive Machine Learning Framework for Benchmarking Complex Multiphase Flows","version":2},"reference_index":47,"source":"arxiv_source","source_observed_at":"2026-08-01T05:18:02.025258Z"},"links":{"citing_paper":"/paper/2607.22280"},"observation_digest":"sha256:38e610d48cc79c5e66d2c7cc85c863d0367c6ad27661c397a018a7692e031cb1","observation_id":"62025016-fa8a-4b20-af20-31f288204474","resolution":{"observed_at":"2026-08-01T05:18:02.025258Z","resolver_source":null,"status":"unresolved"},"standing_notice":{"events":[],"reason":"canonical_work_link_unavailable","source_receipts":[],"state":"unavailable"}},{"citation":{"cited_paper":null,"cited_work":{"arxiv_id":null,"doi":null,"metadata_source":null,"pith_arxiv_id":null,"snapshot_observed_at":"2026-08-01T05:18:02.082703Z","title":"2013 , publisher =","venue":null,"work_id":null,"year":2013},"citing_paper":{"arxiv_id":"2607.22280","last_updated":"2026-07-27T07:37:39Z","snapshot_observed_at":"2026-08-05T16:41:49.564894Z","submitted_at":"2026-07-24T13:20:01Z","title":"Neptuna: A Comprehensive Machine Learning Framework for Benchmarking Complex Multiphase Flows","version":2},"reference_index":48,"source":"arxiv_source","source_observed_at":"2026-08-01T05:18:02.082703Z"},"links":{"citing_paper":"/paper/2607.22280"},"observation_digest":"sha256:c46b01dafb3cb3a0a04335ee8e5502bc55ec8680e021a1943812e43c2ab93780","observation_id":"26bd5740-e073-43b2-aea8-3b78270abe66","resolution":{"observed_at":"2026-08-01T05:18:02.082703Z","resolver_source":null,"status":"unresolved"},"standing_notice":{"events":[],"reason":"canonical_work_link_unavailable","source_receipts":[],"state":"unavailable"}},{"citation":{"cited_paper":null,"cited_work":{"arxiv_id":null,"doi":null,"metadata_source":null,"pith_arxiv_id":null,"snapshot_observed_at":"2026-08-01T05:18:02.149444Z","title":"Journal of computational physics , volume =","venue":null,"work_id":null,"year":2020},"citing_paper":{"arxiv_id":"2607.22280","last_updated":"2026-07-27T07:37:39Z","snapshot_observed_at":"2026-08-05T16:41:49.564894Z","submitted_at":"2026-07-24T13:20:01Z","title":"Neptuna: A Comprehensive Machine Learning Framework for Benchmarking Complex Multiphase Flows","version":2},"reference_index":49,"source":"arxiv_source","source_observed_at":"2026-08-01T05:18:02.149444Z"},"links":{"citing_paper":"/paper/2607.22280"},"observation_digest":"sha256:07db35444c2f7ec54101e371ada720970a963cd1012cad973e3e3c2a66a4177f","observation_id":"8058bcbd-7773-42f3-907d-151cd108b7ff","resolution":{"observed_at":"2026-08-01T05:18:02.149444Z","resolver_source":null,"status":"unresolved"},"standing_notice":{"events":[],"reason":"canonical_work_link_unavailable","source_receipts":[],"state":"unavailable"}},{"citation":{"cited_paper":null,"cited_work":{"arxiv_id":null,"doi":null,"metadata_source":null,"pith_arxiv_id":null,"snapshot_observed_at":"2026-08-01T05:18:02.264201Z","title":"Journal of computational physics , volume =","venue":null,"work_id":null,"year":1996},"citing_paper":{"arxiv_id":"2607.22280","last_updated":"2026-07-27T07:37:39Z","snapshot_observed_at":"2026-08-05T16:41:49.564894Z","submitted_at":"2026-07-24T13:20:01Z","title":"Neptuna: A Comprehensive Machine Learning Framework for Benchmarking Complex Multiphase Flows","version":2},"reference_index":50,"source":"arxiv_source","source_observed_at":"2026-08-01T05:18:02.264201Z"},"links":{"citing_paper":"/paper/2607.22280"},"observation_digest":"sha256:ec48ca443eafbea4325e2845f6f2891e9530b13b94502678d42efcb09fd10bcd","observation_id":"de1cf66f-9074-4d77-aea5-c169505090af","resolution":{"observed_at":"2026-08-01T05:18:02.264201Z","resolver_source":null,"status":"unresolved"},"standing_notice":{"events":[],"reason":"canonical_work_link_unavailable","source_receipts":[],"state":"unavailable"}},{"citation":{"cited_paper":null,"cited_work":{"arxiv_id":null,"doi":null,"metadata_source":null,"pith_arxiv_id":null,"snapshot_observed_at":"2026-08-01T05:18:02.343513Z","title":"Mathematics of computation , volume =","venue":null,"work_id":null,"year":null},"citing_paper":{"arxiv_id":"2607.22280","last_updated":"2026-07-27T07:37:39Z","snapshot_observed_at":"2026-08-05T16:41:49.564894Z","submitted_at":"2026-07-24T13:20:01Z","title":"Neptuna: A Comprehensive Machine Learning Framework for Benchmarking Complex Multiphase Flows","version":2},"reference_index":51,"source":"arxiv_source","source_observed_at":"2026-08-01T05:18:02.343513Z"},"links":{"citing_paper":"/paper/2607.22280"},"observation_digest":"sha256:45a5d83cf83bcd7d114d46add50c5d2e56ccd9a50a03dde3f672f2ee7c9b1055","observation_id":"56b215b5-efbd-4e4d-9807-106f494545b8","resolution":{"observed_at":"2026-08-01T05:18:02.343513Z","resolver_source":null,"status":"unresolved"},"standing_notice":{"events":[],"reason":"canonical_work_link_unavailable","source_receipts":[],"state":"unavailable"}}],"paper":{"arxiv_id":"2607.22280","last_updated":"2026-07-27T07:37:39Z","latest_version":2,"primary_category":"physics.flu-dyn","snapshot_observed_at":"2026-08-05T16:41:49.564894Z","submitted_at":"2026-07-24T13:20:01Z","title":"Neptuna: A Comprehensive Machine Learning Framework for Benchmarking Complex Multiphase Flows"},"reference_resolution":{"displayed":51,"state_counts":{"malformed_identifier":0,"metadata_mismatch":0,"parse_uncertain":0,"unresolved":51,"verified_exact":0,"verified_fuzzy":0},"total_outbound_references":51},"refusal":"A citation records a reference. It does not transfer a finding from one paper to another.","schema":"pith.paper-citation-record.v1","standing_sources":[{"observed_at":"2026-08-18T06:34:40.430872+00:00","source":"crossref"},{"observed_at":"2026-08-18T06:34:34.496301+00:00","source":"retraction_watch"}],"thesis":"As of 19 August 2026, this Paper Citation Record lists 51 of 51 outbound references and 0 inbound Pith citation observations for arXiv:2607.22280."}