{"as_of":"2026-08-16T03:57:00Z","caps":{"database_statements":6,"inbound":100,"outbound":100},"context_digest":"sha256:947d5af04d3f66f509f0c785cbd4000b9e585ca3809747f21d3cbea5e2b14dcc","coverage":[{"denominator":47,"lane":"reference_resolution","note":"Typed states for the displayed outbound observations.","records_observed":47,"source":"paper_references, paper_reference_links","source_observed_at":"2026-08-11T17:11:04.824145Z","state":"measured"},{"denominator":47,"lane":"standing_notices","note":"One-hop event checks from named stored sources.","records_observed":47,"source":"scholarly_work_events, retraction_status_cache","source_observed_at":"2026-08-15T06:32:42.880941+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/2412.09369/citation-record","integrity":"/paper/2412.09369/integrity","json":"/paper/2412.09369/citation-record.json","paper":"/paper/2412.09369"},"outbound":[{"citation":{"cited_paper":null,"cited_work":{"arxiv_id":null,"doi":null,"metadata_source":null,"pith_arxiv_id":null,"snapshot_observed_at":"2026-08-11T17:11:04.558269Z","title":"Machine learning: Trends, perspectives, and prospects","venue":null,"work_id":null,"year":2015},"citing_paper":{"arxiv_id":"2412.09369","last_updated":"2024-12-12T15:37:02Z","snapshot_observed_at":"2026-08-12T01:38:56.032653Z","submitted_at":"2024-12-12T15:37:02Z","title":"Distribution free uncertainty quantification in neuroscience-inspired deep operators","version":1},"reference_index":1,"source":"pdf_text","source_observed_at":"2026-08-11T17:11:04.558269Z"},"links":{"citing_paper":"/paper/2412.09369"},"observation_digest":"sha256:bf062e6973757720103e7b0bbe75cbf37ea12b7cffe1238b0d9b44fe0d239988","observation_id":"ada34170-3827-4805-ab9c-79e043ceada2","resolution":{"observed_at":"2026-08-11T17:11:04.558269Z","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":"2026-08-11T17:11:04.570811Z","title":"Machine learning algorithms-a review","venue":null,"work_id":null,"year":2020},"citing_paper":{"arxiv_id":"2412.09369","last_updated":"2024-12-12T15:37:02Z","snapshot_observed_at":"2026-08-12T01:38:56.032653Z","submitted_at":"2024-12-12T15:37:02Z","title":"Distribution free uncertainty quantification in neuroscience-inspired deep operators","version":1},"reference_index":2,"source":"pdf_text","source_observed_at":"2026-08-11T17:11:04.570811Z"},"links":{"citing_paper":"/paper/2412.09369"},"observation_digest":"sha256:ec4baf378054d9364e6c0cf5aec572c357ea0e441d559c8fc30f2868d1a6da25","observation_id":"57408585-dcbd-429f-b2a9-fc8aeecbea0f","resolution":{"observed_at":"2026-08-11T17:11:04.570811Z","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":"raw_reference","pith_arxiv_id":null,"snapshot_observed_at":"2026-08-11T17:11:20.589550Z","title":"Machine learning","venue":null,"work_id":"cb27fdb6-4990-433f-a8d0-0ea1cc852340","year":2021},"citing_paper":{"arxiv_id":"2412.09369","last_updated":"2024-12-12T15:37:02Z","snapshot_observed_at":"2026-08-12T01:38:56.032653Z","submitted_at":"2024-12-12T15:37:02Z","title":"Distribution free uncertainty quantification in neuroscience-inspired deep operators","version":1},"reference_index":3,"source":"pdf_text","source_observed_at":"2026-08-11T17:11:04.574619Z"},"links":{"citing_paper":"/paper/2412.09369"},"observation_digest":"sha256:9e00f32502d4ff787409c6febf243a1f21c831284ff1bc3f76102b7dfdf293ee","observation_id":"bb038cf3-abb2-41f7-8f99-a7aa39b3c7d7","resolution":{"observed_at":"2026-08-11T17:11:20.594136Z","resolver_source":"raw_fallback","status":"verified_fuzzy"},"standing_notice":{"events":[],"observation":"No event found in the named queried sources as of 2026-08-15T06:32:42.880941+00:00.","reason":null,"source_receipts":[{"observed_at":"2026-08-15T06:32:42.880941+00:00","source":"crossref"},{"observed_at":"2026-08-15T06:32:39.529945+00:00","source":"retraction_watch"}],"state":"measured"}},{"citation":{"cited_paper":null,"cited_work":{"arxiv_id":null,"doi":null,"metadata_source":"raw_reference","pith_arxiv_id":null,"snapshot_observed_at":"2026-08-11T17:11:20.572859Z","title":"Reliability-based design optimization using kriging surrogates and subset simulation","venue":null,"work_id":"6cb4ab0c-512d-4ee4-b8e9-d69d6e6c5724","year":2011},"citing_paper":{"arxiv_id":"2412.09369","last_updated":"2024-12-12T15:37:02Z","snapshot_observed_at":"2026-08-12T01:38:56.032653Z","submitted_at":"2024-12-12T15:37:02Z","title":"Distribution free uncertainty quantification in neuroscience-inspired deep operators","version":1},"reference_index":4,"source":"pdf_text","source_observed_at":"2026-08-11T17:11:04.578941Z"},"links":{"citing_paper":"/paper/2412.09369"},"observation_digest":"sha256:76e1f7308b3267262c18b565795705e47b409acc4a7800438787908f59ddf0b9","observation_id":"a67f3d79-c550-4f95-aa73-441e09ac7e32","resolution":{"observed_at":"2026-08-11T17:11:20.578872Z","resolver_source":"raw_fallback","status":"verified_fuzzy"},"standing_notice":{"events":[],"observation":"No event found in the named queried sources as of 2026-08-15T06:32:42.880941+00:00.","reason":null,"source_receipts":[{"observed_at":"2026-08-15T06:32:42.880941+00:00","source":"crossref"},{"observed_at":"2026-08-15T06:32:39.529945+00:00","source":"retraction_watch"}],"state":"measured"}},{"citation":{"cited_paper":null,"cited_work":{"arxiv_id":null,"doi":null,"metadata_source":"raw_reference","pith_arxiv_id":null,"snapshot_observed_at":"2026-08-11T17:11:20.552395Z","title":"Support vector machine in structural reliability analysis: A review.Reliability Engineering & System Safety, 233:109126, 2023","venue":null,"work_id":"e84fa9e3-cfe7-43e6-bd7e-6e80fc56234a","year":2023},"citing_paper":{"arxiv_id":"2412.09369","last_updated":"2024-12-12T15:37:02Z","snapshot_observed_at":"2026-08-12T01:38:56.032653Z","submitted_at":"2024-12-12T15:37:02Z","title":"Distribution free uncertainty quantification in neuroscience-inspired deep operators","version":1},"reference_index":5,"source":"pdf_text","source_observed_at":"2026-08-11T17:11:04.584042Z"},"links":{"citing_paper":"/paper/2412.09369"},"observation_digest":"sha256:43e28cefc4c0fbf50331ed1ee392bf8585132b1ea3fb7899dc188a5d7960832f","observation_id":"5160b3dc-ecb0-41f8-bff1-fee9d18f40d2","resolution":{"observed_at":"2026-08-11T17:11:20.558670Z","resolver_source":"raw_fallback","status":"verified_fuzzy"},"standing_notice":{"events":[],"observation":"No event found in the named queried sources as of 2026-08-15T06:32:42.880941+00:00.","reason":null,"source_receipts":[{"observed_at":"2026-08-15T06:32:42.880941+00:00","source":"crossref"},{"observed_at":"2026-08-15T06:32:39.529945+00:00","source":"retraction_watch"}],"state":"measured"}},{"citation":{"cited_paper":null,"cited_work":{"arxiv_id":null,"doi":null,"metadata_source":"raw_reference","pith_arxiv_id":null,"snapshot_observed_at":"2026-08-11T17:11:20.536682Z","title":"Reliability analyses of underground tunnels by an adaptive support vector regression model","venue":null,"work_id":"c03747d3-3675-4f4a-9656-8fd9e120706e","year":2024},"citing_paper":{"arxiv_id":"2412.09369","last_updated":"2024-12-12T15:37:02Z","snapshot_observed_at":"2026-08-12T01:38:56.032653Z","submitted_at":"2024-12-12T15:37:02Z","title":"Distribution free uncertainty quantification in neuroscience-inspired deep operators","version":1},"reference_index":6,"source":"pdf_text","source_observed_at":"2026-08-11T17:11:04.590633Z"},"links":{"citing_paper":"/paper/2412.09369"},"observation_digest":"sha256:2f98f02e659b28c48c93d84fbf33f61e998251237983939dbfbe22773052a96f","observation_id":"2394f216-b775-41ed-b997-906fba1234a7","resolution":{"observed_at":"2026-08-11T17:11:20.541556Z","resolver_source":"raw_fallback","status":"verified_fuzzy"},"standing_notice":{"events":[],"observation":"No event found in the named queried sources as of 2026-08-15T06:32:42.880941+00:00.","reason":null,"source_receipts":[{"observed_at":"2026-08-15T06:32:42.880941+00:00","source":"crossref"},{"observed_at":"2026-08-15T06:32:39.529945+00:00","source":"retraction_watch"}],"state":"measured"}},{"citation":{"cited_paper":null,"cited_work":{"arxiv_id":null,"doi":null,"metadata_source":"raw_reference","pith_arxiv_id":null,"snapshot_observed_at":"2026-08-11T17:11:20.521602Z","title":"Explainable, interpretable, and trustworthy ai for an intelli- gent digital twin: A case study on remaining useful life","venue":null,"work_id":"058f67de-1eb9-4e7b-abd3-4a0f7b0878b6","year":2024},"citing_paper":{"arxiv_id":"2412.09369","last_updated":"2024-12-12T15:37:02Z","snapshot_observed_at":"2026-08-12T01:38:56.032653Z","submitted_at":"2024-12-12T15:37:02Z","title":"Distribution free uncertainty quantification in neuroscience-inspired deep operators","version":1},"reference_index":7,"source":"pdf_text","source_observed_at":"2026-08-11T17:11:04.603130Z"},"links":{"citing_paper":"/paper/2412.09369"},"observation_digest":"sha256:3ee63c847ecda91d3adb98a3c615602ef5bc1e43795418250e75d79c4f613c71","observation_id":"1c0826a3-ab44-48d9-880d-aae52c33754b","resolution":{"observed_at":"2026-08-11T17:11:20.526092Z","resolver_source":"raw_fallback","status":"verified_fuzzy"},"standing_notice":{"events":[],"observation":"No event found in the named queried sources as of 2026-08-15T06:32:42.880941+00:00.","reason":null,"source_receipts":[{"observed_at":"2026-08-15T06:32:42.880941+00:00","source":"crossref"},{"observed_at":"2026-08-15T06:32:39.529945+00:00","source":"retraction_watch"}],"state":"measured"}},{"citation":{"cited_paper":null,"cited_work":{"arxiv_id":null,"doi":null,"metadata_source":null,"pith_arxiv_id":null,"snapshot_observed_at":"2026-08-11T17:11:04.608269Z","title":"Deep neural operator-driven real-time inference to enable digital twin solutions for nuclear energy systems","venue":null,"work_id":null,"year":2024},"citing_paper":{"arxiv_id":"2412.09369","last_updated":"2024-12-12T15:37:02Z","snapshot_observed_at":"2026-08-12T01:38:56.032653Z","submitted_at":"2024-12-12T15:37:02Z","title":"Distribution free uncertainty quantification in neuroscience-inspired deep operators","version":1},"reference_index":8,"source":"pdf_text","source_observed_at":"2026-08-11T17:11:04.608269Z"},"links":{"citing_paper":"/paper/2412.09369"},"observation_digest":"sha256:41e9f103e580742c7a9b12729992dfa057a0fdc4f6e37e2388296200fcdf5b6a","observation_id":"7e9ce458-06da-41c5-99ba-7ce3f95145f7","resolution":{"observed_at":"2026-08-11T17:11:04.608269Z","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":"raw_reference","pith_arxiv_id":null,"snapshot_observed_at":"2026-08-11T17:11:20.488008Z","title":"Advances in computational intelligence of polymer composite materials: machine learning assisted mod- eling, analysis and design","venue":null,"work_id":"1e7e8e3d-bf79-4e4a-8e1d-134c984ebf65","year":2022},"citing_paper":{"arxiv_id":"2412.09369","last_updated":"2024-12-12T15:37:02Z","snapshot_observed_at":"2026-08-12T01:38:56.032653Z","submitted_at":"2024-12-12T15:37:02Z","title":"Distribution free uncertainty quantification in neuroscience-inspired deep operators","version":1},"reference_index":9,"source":"pdf_text","source_observed_at":"2026-08-11T17:11:04.612757Z"},"links":{"citing_paper":"/paper/2412.09369"},"observation_digest":"sha256:c131f294b865fc88703d6fe99e0439e8743740b399c30203c2a90a923101b9a6","observation_id":"b7a7cf29-40a9-4ba2-9188-6c6e91908cf7","resolution":{"observed_at":"2026-08-11T17:11:20.495553Z","resolver_source":"raw_fallback","status":"verified_fuzzy"},"standing_notice":{"events":[],"observation":"No event found in the named queried sources as of 2026-08-15T06:32:42.880941+00:00.","reason":null,"source_receipts":[{"observed_at":"2026-08-15T06:32:42.880941+00:00","source":"crossref"},{"observed_at":"2026-08-15T06:32:39.529945+00:00","source":"retraction_watch"}],"state":"measured"}},{"citation":{"cited_paper":null,"cited_work":{"arxiv_id":null,"doi":null,"metadata_source":"raw_reference","pith_arxiv_id":null,"snapshot_observed_at":"2026-08-11T17:11:20.465500Z","title":"Machine learning based digital twin for stochastic nonlinear multi-degree of freedom dynamical system","venue":null,"work_id":"60310af3-c834-4050-a855-1f6bebf19728","year":2021},"citing_paper":{"arxiv_id":"2412.09369","last_updated":"2024-12-12T15:37:02Z","snapshot_observed_at":"2026-08-12T01:38:56.032653Z","submitted_at":"2024-12-12T15:37:02Z","title":"Distribution free uncertainty quantification in neuroscience-inspired deep operators","version":1},"reference_index":10,"source":"pdf_text","source_observed_at":"2026-08-11T17:11:04.617888Z"},"links":{"citing_paper":"/paper/2412.09369"},"observation_digest":"sha256:fb08a6bba2bac4ee4ad2694f77e3d5f272fd6f1dd76f430f1de24f48288a6590","observation_id":"0481917f-8a84-49fc-a3af-38bcd95baa19","resolution":{"observed_at":"2026-08-11T17:11:20.471123Z","resolver_source":"raw_fallback","status":"verified_fuzzy"},"standing_notice":{"events":[],"observation":"No event found in the named queried sources as of 2026-08-15T06:32:42.880941+00:00.","reason":null,"source_receipts":[{"observed_at":"2026-08-15T06:32:42.880941+00:00","source":"crossref"},{"observed_at":"2026-08-15T06:32:39.529945+00:00","source":"retraction_watch"}],"state":"measured"}},{"citation":{"cited_paper":{"arxiv_id":"2411.03671","last_updated":"2025-01-30T16:30:15Z","snapshot_observed_at":"2026-08-12T22:06:45.760208Z","submitted_at":"2024-11-06T05:10:20Z","title":"Energy-based physics-informed neural network for frictionless contact problems under large deformation","version":2},"cited_work":{"arxiv_id":"2411.03671","doi":null,"metadata_source":"pith","pith_arxiv_id":"2411.03671","snapshot_observed_at":"2026-08-11T17:11:20.050158Z","title":"Energy-based physics-informed neural network for frictionless contact problems under large deformation","venue":"cs.CE","work_id":"c9f381e0-6736-44ec-91a8-f419655ca7f9","year":2024},"citing_paper":{"arxiv_id":"2412.09369","last_updated":"2024-12-12T15:37:02Z","snapshot_observed_at":"2026-08-12T01:38:56.032653Z","submitted_at":"2024-12-12T15:37:02Z","title":"Distribution free uncertainty quantification in neuroscience-inspired deep operators","version":1},"reference_index":11,"source":"pdf_text","source_observed_at":"2026-08-11T17:11:04.625825Z"},"links":{"cited_paper":"/paper/2411.03671","citing_paper":"/paper/2412.09369"},"observation_digest":"sha256:c1e082138c163b446301eb15d5218d76f30415229f6a464fa9aab2906eff01d1","observation_id":"30666540-ca72-4adc-b271-7a9f37f33a19","resolution":{"observed_at":"2026-08-11T17:11:20.056106Z","resolver_source":"local_arxiv","status":"verified_exact"},"standing_notice":{"events":[],"observation":"No event found in the named queried sources as of 2026-08-15T06:32:42.880941+00:00.","reason":null,"source_receipts":[{"observed_at":"2026-08-15T06:32:42.880941+00:00","source":"crossref"},{"observed_at":"2026-08-15T06:32:39.529945+00:00","source":"retraction_watch"}],"state":"measured"}},{"citation":{"cited_paper":null,"cited_work":{"arxiv_id":null,"doi":null,"metadata_source":null,"pith_arxiv_id":null,"snapshot_observed_at":"2026-08-11T17:11:04.634698Z","title":"Artificial intelligence for partial differential equa- tions in computational mechanics: A review","venue":null,"work_id":null,"year":null},"citing_paper":{"arxiv_id":"2412.09369","last_updated":"2024-12-12T15:37:02Z","snapshot_observed_at":"2026-08-12T01:38:56.032653Z","submitted_at":"2024-12-12T15:37:02Z","title":"Distribution free uncertainty quantification in neuroscience-inspired deep operators","version":1},"reference_index":12,"source":"pdf_text","source_observed_at":"2026-08-11T17:11:04.634698Z"},"links":{"citing_paper":"/paper/2412.09369"},"observation_digest":"sha256:1aafc48bc239b00f8efad169c179dece9e632033c2ea719c28daa7fb3d728202","observation_id":"ad6f2b7e-558b-4c14-a2dc-e0fe5547f6ba","resolution":{"observed_at":"2026-08-11T17:11:04.634698Z","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":"raw_reference","pith_arxiv_id":null,"snapshot_observed_at":"2026-08-11T17:11:20.450186Z","title":"A novel machine-learning framework with a moving platform for maritime drift calculations","venue":null,"work_id":"1e6ea51f-a28c-448d-9c9a-e7a1da43d9b1","year":2022},"citing_paper":{"arxiv_id":"2412.09369","last_updated":"2024-12-12T15:37:02Z","snapshot_observed_at":"2026-08-12T01:38:56.032653Z","submitted_at":"2024-12-12T15:37:02Z","title":"Distribution free uncertainty quantification in neuroscience-inspired deep operators","version":1},"reference_index":13,"source":"pdf_text","source_observed_at":"2026-08-11T17:11:04.639230Z"},"links":{"citing_paper":"/paper/2412.09369"},"observation_digest":"sha256:f0be7760efce9e87b7bebdc3d60a9aba07561a2cd431d132f1a1649d9f67aba9","observation_id":"61fc98ef-e951-45fb-9e0b-357643131e8a","resolution":{"observed_at":"2026-08-11T17:11:20.455061Z","resolver_source":"raw_fallback","status":"verified_fuzzy"},"standing_notice":{"events":[],"observation":"No event found in the named queried sources as of 2026-08-15T06:32:42.880941+00:00.","reason":null,"source_receipts":[{"observed_at":"2026-08-15T06:32:42.880941+00:00","source":"crossref"},{"observed_at":"2026-08-15T06:32:39.529945+00:00","source":"retraction_watch"}],"state":"measured"}},{"citation":{"cited_paper":null,"cited_work":{"arxiv_id":null,"doi":null,"metadata_source":"raw_reference","pith_arxiv_id":null,"snapshot_observed_at":"2026-08-11T17:11:20.434928Z","title":"Accelerated neural network solvers of navier stokes equations for turbulent flows","venue":null,"work_id":"101136eb-74ce-435a-9688-632539f4bec5","year":2024},"citing_paper":{"arxiv_id":"2412.09369","last_updated":"2024-12-12T15:37:02Z","snapshot_observed_at":"2026-08-12T01:38:56.032653Z","submitted_at":"2024-12-12T15:37:02Z","title":"Distribution free uncertainty quantification in neuroscience-inspired deep operators","version":1},"reference_index":14,"source":"pdf_text","source_observed_at":"2026-08-11T17:11:04.643681Z"},"links":{"citing_paper":"/paper/2412.09369"},"observation_digest":"sha256:e779cbbad4030d3612dfe9308184ee7d69fa6ce6874bc3e73fb5f8d74679a8d9","observation_id":"3a7f2adb-cd33-4ae6-bfc3-1191a1c68475","resolution":{"observed_at":"2026-08-11T17:11:20.440281Z","resolver_source":"raw_fallback","status":"verified_fuzzy"},"standing_notice":{"events":[],"observation":"No event found in the named queried sources as of 2026-08-15T06:32:42.880941+00:00.","reason":null,"source_receipts":[{"observed_at":"2026-08-15T06:32:42.880941+00:00","source":"crossref"},{"observed_at":"2026-08-15T06:32:39.529945+00:00","source":"retraction_watch"}],"state":"measured"}},{"citation":{"cited_paper":null,"cited_work":{"arxiv_id":null,"doi":null,"metadata_source":null,"pith_arxiv_id":null,"snapshot_observed_at":"2026-08-11T17:11:04.649953Z","title":"Scientific machine learning through physics–informed neural networks: Where we are and what’s next","venue":null,"work_id":null,"year":2022},"citing_paper":{"arxiv_id":"2412.09369","last_updated":"2024-12-12T15:37:02Z","snapshot_observed_at":"2026-08-12T01:38:56.032653Z","submitted_at":"2024-12-12T15:37:02Z","title":"Distribution free uncertainty quantification in neuroscience-inspired deep operators","version":1},"reference_index":15,"source":"pdf_text","source_observed_at":"2026-08-11T17:11:04.649953Z"},"links":{"citing_paper":"/paper/2412.09369"},"observation_digest":"sha256:1bb259d32a458a50edfea7271dcb7da182e2ef3e0a6e744ba73c790d8274ef0f","observation_id":"2da97762-e8b7-4b00-b5e9-41a0abf68fdd","resolution":{"observed_at":"2026-08-11T17:11:04.649953Z","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":"raw_reference","pith_arxiv_id":null,"snapshot_observed_at":"2026-08-11T17:11:20.413788Z","title":"Scientific machine learning bench- marks","venue":null,"work_id":"efa322d5-5115-4408-b0aa-2cf2303b03d9","year":2022},"citing_paper":{"arxiv_id":"2412.09369","last_updated":"2024-12-12T15:37:02Z","snapshot_observed_at":"2026-08-12T01:38:56.032653Z","submitted_at":"2024-12-12T15:37:02Z","title":"Distribution free uncertainty quantification in neuroscience-inspired deep operators","version":1},"reference_index":16,"source":"pdf_text","source_observed_at":"2026-08-11T17:11:04.657625Z"},"links":{"citing_paper":"/paper/2412.09369"},"observation_digest":"sha256:1589e6479c58805ede553976c4be8fd57bfc4de0464b34f372125d1b12bcdcb4","observation_id":"ade95d98-3f97-4949-9115-fe6c40c80600","resolution":{"observed_at":"2026-08-11T17:11:20.418158Z","resolver_source":"raw_fallback","status":"verified_fuzzy"},"standing_notice":{"events":[],"observation":"No event found in the named queried sources as of 2026-08-15T06:32:42.880941+00:00.","reason":null,"source_receipts":[{"observed_at":"2026-08-15T06:32:42.880941+00:00","source":"crossref"},{"observed_at":"2026-08-15T06:32:39.529945+00:00","source":"retraction_watch"}],"state":"measured"}},{"citation":{"cited_paper":null,"cited_work":{"arxiv_id":null,"doi":null,"metadata_source":null,"pith_arxiv_id":null,"snapshot_observed_at":"2026-08-11T17:11:04.662580Z","title":"Machine learning and big scientific data","venue":null,"work_id":null,"year":2020},"citing_paper":{"arxiv_id":"2412.09369","last_updated":"2024-12-12T15:37:02Z","snapshot_observed_at":"2026-08-12T01:38:56.032653Z","submitted_at":"2024-12-12T15:37:02Z","title":"Distribution free uncertainty quantification in neuroscience-inspired deep operators","version":1},"reference_index":17,"source":"pdf_text","source_observed_at":"2026-08-11T17:11:04.662580Z"},"links":{"citing_paper":"/paper/2412.09369"},"observation_digest":"sha256:11b28457c1f2b1c9a71812204718506695547f469a750cfcc7403738106e7c3d","observation_id":"bd1edc05-e44c-4155-ba7c-71e1b70ffea9","resolution":{"observed_at":"2026-08-11T17:11:04.662580Z","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":"2026-08-11T17:11:04.667463Z","title":"An introduction to neural networks","venue":null,"work_id":null,"year":2018},"citing_paper":{"arxiv_id":"2412.09369","last_updated":"2024-12-12T15:37:02Z","snapshot_observed_at":"2026-08-12T01:38:56.032653Z","submitted_at":"2024-12-12T15:37:02Z","title":"Distribution free uncertainty quantification in neuroscience-inspired deep operators","version":1},"reference_index":18,"source":"pdf_text","source_observed_at":"2026-08-11T17:11:04.667463Z"},"links":{"citing_paper":"/paper/2412.09369"},"observation_digest":"sha256:a2a8a837585603f4820b0630b073956fb39ff0e6cd4c2cf27fd993fa35435f6a","observation_id":"3c91592b-d1e9-4906-b4a9-215b6420780e","resolution":{"observed_at":"2026-08-11T17:11:04.667463Z","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":"2026-08-11T17:11:04.671891Z","title":"Deep learning","venue":null,"work_id":null,"year":2015},"citing_paper":{"arxiv_id":"2412.09369","last_updated":"2024-12-12T15:37:02Z","snapshot_observed_at":"2026-08-12T01:38:56.032653Z","submitted_at":"2024-12-12T15:37:02Z","title":"Distribution free uncertainty quantification in neuroscience-inspired deep operators","version":1},"reference_index":19,"source":"pdf_text","source_observed_at":"2026-08-11T17:11:04.671891Z"},"links":{"citing_paper":"/paper/2412.09369"},"observation_digest":"sha256:e3742e8cec90fb8a77ad3e5e87a6220c9b0eadbd1e27c6ffb92521be2bf52df9","observation_id":"87e7403a-84de-437a-8e76-1bcc2eaa7dea","resolution":{"observed_at":"2026-08-11T17:11:04.671891Z","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":"raw_reference","pith_arxiv_id":null,"snapshot_observed_at":"2026-08-11T17:11:20.368397Z","title":"State-of-the-art in artificial neural network applications: A survey","venue":null,"work_id":"072b73e2-ed39-4178-a064-5bb043c0755d","year":2018},"citing_paper":{"arxiv_id":"2412.09369","last_updated":"2024-12-12T15:37:02Z","snapshot_observed_at":"2026-08-12T01:38:56.032653Z","submitted_at":"2024-12-12T15:37:02Z","title":"Distribution free uncertainty quantification in neuroscience-inspired deep operators","version":1},"reference_index":20,"source":"pdf_text","source_observed_at":"2026-08-11T17:11:04.678226Z"},"links":{"citing_paper":"/paper/2412.09369"},"observation_digest":"sha256:00512834526e8d01a875a0f5a12a1f2483d117671db7fb1c8d1cad602b28b42d","observation_id":"65f49b97-1197-48da-b213-9711282978bf","resolution":{"observed_at":"2026-08-11T17:11:20.373298Z","resolver_source":"raw_fallback","status":"verified_fuzzy"},"standing_notice":{"events":[],"observation":"No event found in the named queried sources as of 2026-08-15T06:32:42.880941+00:00.","reason":null,"source_receipts":[{"observed_at":"2026-08-15T06:32:42.880941+00:00","source":"crossref"},{"observed_at":"2026-08-15T06:32:39.529945+00:00","source":"retraction_watch"}],"state":"measured"}},{"citation":{"cited_paper":null,"cited_work":{"arxiv_id":null,"doi":null,"metadata_source":"raw_reference","pith_arxiv_id":null,"snapshot_observed_at":"2026-08-11T17:11:20.353709Z","title":"Learning nonlinear op- erators via deeponet based on the universal approximation theorem of operators","venue":null,"work_id":"ceb47887-0013-4e71-969a-edc5db8d9cf4","year":2021},"citing_paper":{"arxiv_id":"2412.09369","last_updated":"2024-12-12T15:37:02Z","snapshot_observed_at":"2026-08-12T01:38:56.032653Z","submitted_at":"2024-12-12T15:37:02Z","title":"Distribution free uncertainty quantification in neuroscience-inspired deep operators","version":1},"reference_index":21,"source":"pdf_text","source_observed_at":"2026-08-11T17:11:04.685982Z"},"links":{"citing_paper":"/paper/2412.09369"},"observation_digest":"sha256:5484303a73f6a526e192914fff1a97454ab4f3ac39886a3ad0400ab18981cd5b","observation_id":"7d2e94d7-798b-4db8-ac0d-c01dabdc1bfe","resolution":{"observed_at":"2026-08-11T17:11:20.358426Z","resolver_source":"raw_fallback","status":"verified_fuzzy"},"standing_notice":{"events":[],"observation":"No event found in the named queried sources as of 2026-08-15T06:32:42.880941+00:00.","reason":null,"source_receipts":[{"observed_at":"2026-08-15T06:32:42.880941+00:00","source":"crossref"},{"observed_at":"2026-08-15T06:32:39.529945+00:00","source":"retraction_watch"}],"state":"measured"}},{"citation":{"cited_paper":{"arxiv_id":"2010.08895","last_updated":"2021-05-17T03:12:33Z","snapshot_observed_at":"2026-08-14T20:53:04.124337Z","submitted_at":"2020-10-18T00:34:21Z","title":"Fourier Neural Operator for Parametric Partial Differential Equations","version":3},"cited_work":{"arxiv_id":null,"doi":null,"metadata_source":null,"pith_arxiv_id":"2010.08895","snapshot_observed_at":"2026-08-11T17:11:04.689984Z","title":"Fourier neural operator for parametric partial differential equations","venue":null,"work_id":null,"year":2010},"citing_paper":{"arxiv_id":"2412.09369","last_updated":"2024-12-12T15:37:02Z","snapshot_observed_at":"2026-08-12T01:38:56.032653Z","submitted_at":"2024-12-12T15:37:02Z","title":"Distribution free uncertainty quantification in neuroscience-inspired deep operators","version":1},"reference_index":22,"source":"pdf_text","source_observed_at":"2026-08-11T17:11:04.689984Z"},"links":{"cited_paper":"/paper/2010.08895","citing_paper":"/paper/2412.09369"},"observation_digest":"sha256:041e557513d0f627c8038979229fc5bffb1048a2e563a0a5627eb29cc110c629","observation_id":"cb21424e-13ca-4115-bfe2-e916ef9f7d0e","resolution":{"observed_at":"2026-08-11T17:11:04.689984Z","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":"2026-08-11T17:11:04.694428Z","title":"Wavelet neural operator for solving parametric partial differential equations in computational mechanics problems","venue":null,"work_id":null,"year":2023},"citing_paper":{"arxiv_id":"2412.09369","last_updated":"2024-12-12T15:37:02Z","snapshot_observed_at":"2026-08-12T01:38:56.032653Z","submitted_at":"2024-12-12T15:37:02Z","title":"Distribution free uncertainty quantification in neuroscience-inspired deep operators","version":1},"reference_index":23,"source":"pdf_text","source_observed_at":"2026-08-11T17:11:04.694428Z"},"links":{"citing_paper":"/paper/2412.09369"},"observation_digest":"sha256:ccf6aae73be05fc565ecc8c62435ce002df6e94d43b50e860ea3a7a8b1261a33","observation_id":"d1903196-b992-4a31-94e1-4a6b72edba33","resolution":{"observed_at":"2026-08-11T17:11:04.694428Z","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":"2026-08-11T17:11:04.700028Z","title":"Neural operator: Learning maps between function spaces with applications to pdes","venue":null,"work_id":null,"year":2023},"citing_paper":{"arxiv_id":"2412.09369","last_updated":"2024-12-12T15:37:02Z","snapshot_observed_at":"2026-08-12T01:38:56.032653Z","submitted_at":"2024-12-12T15:37:02Z","title":"Distribution free uncertainty quantification in neuroscience-inspired deep operators","version":1},"reference_index":24,"source":"pdf_text","source_observed_at":"2026-08-11T17:11:04.700028Z"},"links":{"citing_paper":"/paper/2412.09369"},"observation_digest":"sha256:083fe8b1ad59a134154b9ef8834c12a51598f3b057eff734f9a9d53cdb308ad2","observation_id":"9c4363d9-7965-454b-9135-a8cb0a63c056","resolution":{"observed_at":"2026-08-11T17:11:04.700028Z","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":"raw_reference","pith_arxiv_id":null,"snapshot_observed_at":"2026-08-11T17:11:20.315245Z","title":"Introduction to finite element methods","venue":null,"work_id":"aadca607-c407-4ace-9a8f-540937934caa","year":2004},"citing_paper":{"arxiv_id":"2412.09369","last_updated":"2024-12-12T15:37:02Z","snapshot_observed_at":"2026-08-12T01:38:56.032653Z","submitted_at":"2024-12-12T15:37:02Z","title":"Distribution free uncertainty quantification in neuroscience-inspired deep operators","version":1},"reference_index":25,"source":"pdf_text","source_observed_at":"2026-08-11T17:11:04.704615Z"},"links":{"citing_paper":"/paper/2412.09369"},"observation_digest":"sha256:b2cea090d39a4667491055c7fdbd83db9f1171c604a251ddfe92bb346ad7a2fd","observation_id":"585e10c3-4817-406b-8253-f4ae86f48cb3","resolution":{"observed_at":"2026-08-11T17:11:20.320931Z","resolver_source":"raw_fallback","status":"verified_fuzzy"},"standing_notice":{"events":[],"observation":"No event found in the named queried sources as of 2026-08-15T06:32:42.880941+00:00.","reason":null,"source_receipts":[{"observed_at":"2026-08-15T06:32:42.880941+00:00","source":"crossref"},{"observed_at":"2026-08-15T06:32:39.529945+00:00","source":"retraction_watch"}],"state":"measured"}},{"citation":{"cited_paper":null,"cited_work":{"arxiv_id":null,"doi":null,"metadata_source":"raw_reference","pith_arxiv_id":null,"snapshot_observed_at":"2026-08-11T17:11:20.297330Z","title":"The finite element method in engineering","venue":null,"work_id":"c0818a4a-2d96-4dab-8e1c-b4ac79955a84","year":2010},"citing_paper":{"arxiv_id":"2412.09369","last_updated":"2024-12-12T15:37:02Z","snapshot_observed_at":"2026-08-12T01:38:56.032653Z","submitted_at":"2024-12-12T15:37:02Z","title":"Distribution free uncertainty quantification in neuroscience-inspired deep operators","version":1},"reference_index":26,"source":"pdf_text","source_observed_at":"2026-08-11T17:11:04.708698Z"},"links":{"citing_paper":"/paper/2412.09369"},"observation_digest":"sha256:f7a7b0a40be1596d76efb7b73a9b8806105061e915f30b5b1174595326454aea","observation_id":"46a2e741-6071-4a83-a65e-52372755140c","resolution":{"observed_at":"2026-08-11T17:11:20.301952Z","resolver_source":"raw_fallback","status":"verified_fuzzy"},"standing_notice":{"events":[],"observation":"No event found in the named queried sources as of 2026-08-15T06:32:42.880941+00:00.","reason":null,"source_receipts":[{"observed_at":"2026-08-15T06:32:42.880941+00:00","source":"crossref"},{"observed_at":"2026-08-15T06:32:39.529945+00:00","source":"retraction_watch"}],"state":"measured"}},{"citation":{"cited_paper":null,"cited_work":{"arxiv_id":null,"doi":null,"metadata_source":null,"pith_arxiv_id":null,"snapshot_observed_at":"2026-08-11T17:11:04.714606Z","title":"Neuroscience inspired neural operator for partial differential equations","venue":null,"work_id":null,"year":2024},"citing_paper":{"arxiv_id":"2412.09369","last_updated":"2024-12-12T15:37:02Z","snapshot_observed_at":"2026-08-12T01:38:56.032653Z","submitted_at":"2024-12-12T15:37:02Z","title":"Distribution free uncertainty quantification in neuroscience-inspired deep operators","version":1},"reference_index":27,"source":"pdf_text","source_observed_at":"2026-08-11T17:11:04.714606Z"},"links":{"citing_paper":"/paper/2412.09369"},"observation_digest":"sha256:4fb088035ce8eba3d85c21acd1f438204f8b329f33041e5a43040b1c6cd7a32b","observation_id":"20588422-4d14-4b04-a4fe-6ecfff881d0c","resolution":{"observed_at":"2026-08-11T17:11:04.714606Z","resolver_source":null,"status":"unresolved"},"standing_notice":{"events":[],"reason":"canonical_work_link_unavailable","source_receipts":[],"state":"unavailable"}},{"citation":{"cited_paper":{"arxiv_id":"2205.10130","last_updated":"2022-10-12T17:50:40Z","snapshot_observed_at":"2026-08-15T16:42:31.575551Z","submitted_at":"2022-05-17T15:22:22Z","title":"Spiking Neural Operators for Scientific Machine Learning","version":2},"cited_work":{"arxiv_id":null,"doi":null,"metadata_source":null,"pith_arxiv_id":"2205.10130","snapshot_observed_at":"2026-08-11T17:11:04.720257Z","title":"Spiking neural operators for scientific machine learning","venue":null,"work_id":null,"year":2022},"citing_paper":{"arxiv_id":"2412.09369","last_updated":"2024-12-12T15:37:02Z","snapshot_observed_at":"2026-08-12T01:38:56.032653Z","submitted_at":"2024-12-12T15:37:02Z","title":"Distribution free uncertainty quantification in neuroscience-inspired deep operators","version":1},"reference_index":28,"source":"pdf_text","source_observed_at":"2026-08-11T17:11:04.720257Z"},"links":{"cited_paper":"/paper/2205.10130","citing_paper":"/paper/2412.09369"},"observation_digest":"sha256:df321d8eebccc8b7ccdc6051137d923e7b6efb2b01c818567918cc0b1af522a7","observation_id":"6a3baebb-daac-4f09-b826-90ed030b2ebb","resolution":{"observed_at":"2026-08-11T17:11:04.720257Z","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":"2026-08-11T17:11:04.726163Z","title":"Conformal prediction: A gentle introduction","venue":null,"work_id":null,"year":2023},"citing_paper":{"arxiv_id":"2412.09369","last_updated":"2024-12-12T15:37:02Z","snapshot_observed_at":"2026-08-12T01:38:56.032653Z","submitted_at":"2024-12-12T15:37:02Z","title":"Distribution free uncertainty quantification in neuroscience-inspired deep operators","version":1},"reference_index":29,"source":"pdf_text","source_observed_at":"2026-08-11T17:11:04.726163Z"},"links":{"citing_paper":"/paper/2412.09369"},"observation_digest":"sha256:d18c92daf47f52845a30a3bf09e8e56eec1753633d34ef7ec0540b4dc954c78c","observation_id":"b8421ae6-313b-4fce-b4f6-fd8e4f0a82e8","resolution":{"observed_at":"2026-08-11T17:11:04.726163Z","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":"2026-08-11T17:11:04.732022Z","title":"Distribution-free predictive inference for regression","venue":null,"work_id":null,"year":2018},"citing_paper":{"arxiv_id":"2412.09369","last_updated":"2024-12-12T15:37:02Z","snapshot_observed_at":"2026-08-12T01:38:56.032653Z","submitted_at":"2024-12-12T15:37:02Z","title":"Distribution free uncertainty quantification in neuroscience-inspired deep operators","version":1},"reference_index":30,"source":"pdf_text","source_observed_at":"2026-08-11T17:11:04.732022Z"},"links":{"citing_paper":"/paper/2412.09369"},"observation_digest":"sha256:e08e2c99c618de8ed5b225f6d4fe643954afb47010d24bba397444a00af94dd5","observation_id":"1a6bbced-b556-41a9-b56a-ed7070f2588e","resolution":{"observed_at":"2026-08-11T17:11:04.732022Z","resolver_source":null,"status":"unresolved"},"standing_notice":{"events":[],"reason":"canonical_work_link_unavailable","source_receipts":[],"state":"unavailable"}},{"citation":{"cited_paper":{"arxiv_id":"2402.15406","last_updated":"2024-02-23T16:07:39Z","snapshot_observed_at":"2026-08-14T04:48:29.756311Z","submitted_at":"2024-02-23T16:07:39Z","title":"Conformalized-DeepONet: A Distribution-Free Framework for Uncertainty Quantification in Deep Operator Networks","version":1},"cited_work":{"arxiv_id":null,"doi":null,"metadata_source":null,"pith_arxiv_id":"2402.15406","snapshot_observed_at":"2026-08-11T17:11:04.737610Z","title":"Conformalized-deeponet: A distribution-free framework for uncertainty quantification in deep operator networks","venue":null,"work_id":null,"year":2024},"citing_paper":{"arxiv_id":"2412.09369","last_updated":"2024-12-12T15:37:02Z","snapshot_observed_at":"2026-08-12T01:38:56.032653Z","submitted_at":"2024-12-12T15:37:02Z","title":"Distribution free uncertainty quantification in neuroscience-inspired deep operators","version":1},"reference_index":31,"source":"pdf_text","source_observed_at":"2026-08-11T17:11:04.737610Z"},"links":{"cited_paper":"/paper/2402.15406","citing_paper":"/paper/2412.09369"},"observation_digest":"sha256:3cd9f73b2c36a9dbead37189627f3df8a4e2f5b3328ed9d3356eb0fa33f6cb62","observation_id":"21732308-da53-4731-a183-e22cc5a69d90","resolution":{"observed_at":"2026-08-11T17:11:04.737610Z","resolver_source":null,"status":"unresolved"},"standing_notice":{"events":[],"reason":"canonical_work_link_unavailable","source_receipts":[],"state":"unavailable"}},{"citation":{"cited_paper":{"arxiv_id":"2402.01960","last_updated":"2024-02-06T04:34:47Z","snapshot_observed_at":"2026-08-14T14:15:30.449375Z","submitted_at":"2024-02-02T23:43:28Z","title":"Calibrated Uncertainty Quantification for Operator Learning via Conformal Prediction","version":2},"cited_work":{"arxiv_id":null,"doi":null,"metadata_source":null,"pith_arxiv_id":"2402.01960","snapshot_observed_at":"2026-08-11T17:11:04.747800Z","title":"Calibrated uncertainty quantification for operator learning via conformal prediction","venue":null,"work_id":null,"year":2024},"citing_paper":{"arxiv_id":"2412.09369","last_updated":"2024-12-12T15:37:02Z","snapshot_observed_at":"2026-08-12T01:38:56.032653Z","submitted_at":"2024-12-12T15:37:02Z","title":"Distribution free uncertainty quantification in neuroscience-inspired deep operators","version":1},"reference_index":32,"source":"pdf_text","source_observed_at":"2026-08-11T17:11:04.747800Z"},"links":{"cited_paper":"/paper/2402.01960","citing_paper":"/paper/2412.09369"},"observation_digest":"sha256:19716c8d01f529058bd8840b32c4a15ce0a22346fce28c4c33559904d9d0e84a","observation_id":"eb304c6d-6fb1-463d-b88b-db6f91936870","resolution":{"observed_at":"2026-08-11T17:11:04.747800Z","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":"raw_reference","pith_arxiv_id":null,"snapshot_observed_at":"2026-08-11T17:11:20.252680Z","title":"Hands- on bayesian neural networks—a tutorial for deep learning users","venue":null,"work_id":"2b9a50da-65d7-4033-b939-9c65f35c90d8","year":2022},"citing_paper":{"arxiv_id":"2412.09369","last_updated":"2024-12-12T15:37:02Z","snapshot_observed_at":"2026-08-12T01:38:56.032653Z","submitted_at":"2024-12-12T15:37:02Z","title":"Distribution free uncertainty quantification in neuroscience-inspired deep operators","version":1},"reference_index":33,"source":"pdf_text","source_observed_at":"2026-08-11T17:11:04.752890Z"},"links":{"citing_paper":"/paper/2412.09369"},"observation_digest":"sha256:c8170ae0e4453498ef55fc1d61108857fdde2f99b6f813b52c2678272554a7a2","observation_id":"d0958f02-7952-4dc2-a7eb-e23eab1702bf","resolution":{"observed_at":"2026-08-11T17:11:20.257910Z","resolver_source":"raw_fallback","status":"verified_fuzzy"},"standing_notice":{"events":[],"observation":"No event found in the named queried sources as of 2026-08-15T06:32:42.880941+00:00.","reason":null,"source_receipts":[{"observed_at":"2026-08-15T06:32:42.880941+00:00","source":"crossref"},{"observed_at":"2026-08-15T06:32:39.529945+00:00","source":"retraction_watch"}],"state":"measured"}},{"citation":{"cited_paper":null,"cited_work":{"arxiv_id":null,"doi":null,"metadata_source":"raw_reference","pith_arxiv_id":null,"snapshot_observed_at":"2026-08-11T17:11:20.235925Z","title":"What are bayesian neural network posteriors really like? In International conference on machine learning , pages 4629–4640","venue":null,"work_id":"49784539-7b1a-4bc8-801e-496de5f2e7b6","year":2021},"citing_paper":{"arxiv_id":"2412.09369","last_updated":"2024-12-12T15:37:02Z","snapshot_observed_at":"2026-08-12T01:38:56.032653Z","submitted_at":"2024-12-12T15:37:02Z","title":"Distribution free uncertainty quantification in neuroscience-inspired deep operators","version":1},"reference_index":34,"source":"pdf_text","source_observed_at":"2026-08-11T17:11:04.757309Z"},"links":{"citing_paper":"/paper/2412.09369"},"observation_digest":"sha256:f6b0be78af69fe102c5146793d37e95145aecb6d176a3e1f0d605226053880d9","observation_id":"da6f2d79-38ca-4942-a702-b1925d8c25b2","resolution":{"observed_at":"2026-08-11T17:11:20.242677Z","resolver_source":"raw_fallback","status":"verified_fuzzy"},"standing_notice":{"events":[],"observation":"No event found in the named queried sources as of 2026-08-15T06:32:42.880941+00:00.","reason":null,"source_receipts":[{"observed_at":"2026-08-15T06:32:42.880941+00:00","source":"crossref"},{"observed_at":"2026-08-15T06:32:39.529945+00:00","source":"retraction_watch"}],"state":"measured"}},{"citation":{"cited_paper":null,"cited_work":{"arxiv_id":null,"doi":null,"metadata_source":null,"pith_arxiv_id":null,"snapshot_observed_at":"2026-08-11T17:11:04.762189Z","title":"Weight uncertainty in neural net- work","venue":null,"work_id":null,"year":2015},"citing_paper":{"arxiv_id":"2412.09369","last_updated":"2024-12-12T15:37:02Z","snapshot_observed_at":"2026-08-12T01:38:56.032653Z","submitted_at":"2024-12-12T15:37:02Z","title":"Distribution free uncertainty quantification in neuroscience-inspired deep operators","version":1},"reference_index":35,"source":"pdf_text","source_observed_at":"2026-08-11T17:11:04.762189Z"},"links":{"citing_paper":"/paper/2412.09369"},"observation_digest":"sha256:00a94b4eceeb6a59cd36c45d38d9001f75e70f433fe81b6066112e1491932c6e","observation_id":"d9f48e78-a627-4c09-9a6d-0017e372069f","resolution":{"observed_at":"2026-08-11T17:11:04.762189Z","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":"2026-08-11T17:11:04.767442Z","title":"A tutorial on conformal prediction","venue":null,"work_id":null,"year":2008},"citing_paper":{"arxiv_id":"2412.09369","last_updated":"2024-12-12T15:37:02Z","snapshot_observed_at":"2026-08-12T01:38:56.032653Z","submitted_at":"2024-12-12T15:37:02Z","title":"Distribution free uncertainty quantification in neuroscience-inspired deep operators","version":1},"reference_index":36,"source":"pdf_text","source_observed_at":"2026-08-11T17:11:04.767442Z"},"links":{"citing_paper":"/paper/2412.09369"},"observation_digest":"sha256:bcf60627ca3f8bfe1efa6dfdf8fd768e5de09facd6c3fc7267c9ecaf5bab70ca","observation_id":"2f83537b-a88c-4027-9be7-92f90f808caa","resolution":{"observed_at":"2026-08-11T17:11:04.767442Z","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":"2026-08-11T17:11:04.773484Z","title":"Randomized prior functions for deep reinforcement learning","venue":null,"work_id":null,"year":2018},"citing_paper":{"arxiv_id":"2412.09369","last_updated":"2024-12-12T15:37:02Z","snapshot_observed_at":"2026-08-12T01:38:56.032653Z","submitted_at":"2024-12-12T15:37:02Z","title":"Distribution free uncertainty quantification in neuroscience-inspired deep operators","version":1},"reference_index":37,"source":"pdf_text","source_observed_at":"2026-08-11T17:11:04.773484Z"},"links":{"citing_paper":"/paper/2412.09369"},"observation_digest":"sha256:c471cf9de2553d7403b154d42971419ea0c6e2eab1f63cdd4f1177fb6c5231c6","observation_id":"11afa2a1-6d5a-4b3d-a39d-a51e5f65b830","resolution":{"observed_at":"2026-08-11T17:11:04.773484Z","resolver_source":null,"status":"unresolved"},"standing_notice":{"events":[],"reason":"canonical_work_link_unavailable","source_receipts":[],"state":"unavailable"}},{"citation":{"cited_paper":{"arxiv_id":"2302.01051","last_updated":"2023-02-02T12:28:49Z","snapshot_observed_at":"2026-08-13T12:52:49.743539Z","submitted_at":"2023-02-02T12:28:49Z","title":"Randomized prior wavelet neural operator for uncertainty quantification","version":1},"cited_work":{"arxiv_id":"2302.01051","doi":null,"metadata_source":"pith","pith_arxiv_id":"2302.01051","snapshot_observed_at":"2026-08-11T17:11:04.887372Z","title":"Randomized prior wavelet neural operator for uncertainty quantification","venue":"stat.ML","work_id":"54558f1b-a721-4900-b0bf-5675c2e02beb","year":2023},"citing_paper":{"arxiv_id":"2412.09369","last_updated":"2024-12-12T15:37:02Z","snapshot_observed_at":"2026-08-12T01:38:56.032653Z","submitted_at":"2024-12-12T15:37:02Z","title":"Distribution free uncertainty quantification in neuroscience-inspired deep operators","version":1},"reference_index":38,"source":"pdf_text","source_observed_at":"2026-08-11T17:11:04.778222Z"},"links":{"cited_paper":"/paper/2302.01051","citing_paper":"/paper/2412.09369"},"observation_digest":"sha256:8aae4c031ce94db8987fdd6fedb5497a823b478501bd6a4af033bc9fe90148f6","observation_id":"ae9cdc6b-f68a-4c00-8552-36603f5aa9ac","resolution":{"observed_at":"2026-08-11T17:11:04.892365Z","resolver_source":"local_arxiv","status":"verified_exact"},"standing_notice":{"events":[],"observation":"No event found in the named queried sources as of 2026-08-15T06:32:42.880941+00:00.","reason":null,"source_receipts":[{"observed_at":"2026-08-15T06:32:42.880941+00:00","source":"crossref"},{"observed_at":"2026-08-15T06:32:39.529945+00:00","source":"retraction_watch"}],"state":"measured"}},{"citation":{"cited_paper":null,"cited_work":{"arxiv_id":null,"doi":null,"metadata_source":null,"pith_arxiv_id":null,"snapshot_observed_at":"2026-08-11T17:11:04.783397Z","title":"Gaussian processes for machine learning , volume 2","venue":null,"work_id":null,"year":2006},"citing_paper":{"arxiv_id":"2412.09369","last_updated":"2024-12-12T15:37:02Z","snapshot_observed_at":"2026-08-12T01:38:56.032653Z","submitted_at":"2024-12-12T15:37:02Z","title":"Distribution free uncertainty quantification in neuroscience-inspired deep operators","version":1},"reference_index":39,"source":"pdf_text","source_observed_at":"2026-08-11T17:11:04.783397Z"},"links":{"citing_paper":"/paper/2412.09369"},"observation_digest":"sha256:e369fe1fd3713680b25a44fe6d61b62b1db88f7ffca7d8d895b6515a7e9b77f2","observation_id":"c5efd7ba-7a96-44c4-8d17-88e064c89ddd","resolution":{"observed_at":"2026-08-11T17:11:04.783397Z","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":"2026-08-11T17:11:04.788798Z","title":"A tutorial on gaussian process regression: Modelling, exploring, and exploiting functions","venue":null,"work_id":null,"year":2018},"citing_paper":{"arxiv_id":"2412.09369","last_updated":"2024-12-12T15:37:02Z","snapshot_observed_at":"2026-08-12T01:38:56.032653Z","submitted_at":"2024-12-12T15:37:02Z","title":"Distribution free uncertainty quantification in neuroscience-inspired deep operators","version":1},"reference_index":40,"source":"pdf_text","source_observed_at":"2026-08-11T17:11:04.788798Z"},"links":{"citing_paper":"/paper/2412.09369"},"observation_digest":"sha256:7d4fb149c4f8de28d9b9ff2e24aed0dfbad47cae4ff0f2c3bcc02a179038759d","observation_id":"3d41b026-b835-4176-8dfa-726323671145","resolution":{"observed_at":"2026-08-11T17:11:04.788798Z","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":"raw_reference","pith_arxiv_id":null,"snapshot_observed_at":"2026-08-11T17:11:20.164171Z","title":"Quantile regression","venue":null,"work_id":"2a050da1-5d12-4ca3-8b8d-69ed1931669b","year":2007},"citing_paper":{"arxiv_id":"2412.09369","last_updated":"2024-12-12T15:37:02Z","snapshot_observed_at":"2026-08-12T01:38:56.032653Z","submitted_at":"2024-12-12T15:37:02Z","title":"Distribution free uncertainty quantification in neuroscience-inspired deep operators","version":1},"reference_index":41,"source":"pdf_text","source_observed_at":"2026-08-11T17:11:04.795308Z"},"links":{"citing_paper":"/paper/2412.09369"},"observation_digest":"sha256:d08a8bd9ef0c15b83de4b03ebfe74f4b801833edda8a7fe64538c311a9ec3bad","observation_id":"f8c3ec67-6e6c-45c6-beff-72865edf4876","resolution":{"observed_at":"2026-08-11T17:11:20.168278Z","resolver_source":"raw_fallback","status":"verified_fuzzy"},"standing_notice":{"events":[],"observation":"No event found in the named queried sources as of 2026-08-15T06:32:42.880941+00:00.","reason":null,"source_receipts":[{"observed_at":"2026-08-15T06:32:42.880941+00:00","source":"crossref"},{"observed_at":"2026-08-15T06:32:39.529945+00:00","source":"retraction_watch"}],"state":"measured"}},{"citation":{"cited_paper":null,"cited_work":{"arxiv_id":null,"doi":null,"metadata_source":"raw_reference","pith_arxiv_id":null,"snapshot_observed_at":"2026-08-11T17:11:20.150968Z","title":"Quantile regression","venue":null,"work_id":"70528404-f58a-48b4-b07e-50ff832bf1cc","year":2001},"citing_paper":{"arxiv_id":"2412.09369","last_updated":"2024-12-12T15:37:02Z","snapshot_observed_at":"2026-08-12T01:38:56.032653Z","submitted_at":"2024-12-12T15:37:02Z","title":"Distribution free uncertainty quantification in neuroscience-inspired deep operators","version":1},"reference_index":42,"source":"pdf_text","source_observed_at":"2026-08-11T17:11:04.799829Z"},"links":{"citing_paper":"/paper/2412.09369"},"observation_digest":"sha256:bfdec4fd32f852042959b3027abcb98a5ec863d90b0fdf76590e7b9fcc7791df","observation_id":"8a12affd-6fdf-4560-8a5d-bf57bbea676a","resolution":{"observed_at":"2026-08-11T17:11:20.155417Z","resolver_source":"raw_fallback","status":"verified_fuzzy"},"standing_notice":{"events":[],"observation":"No event found in the named queried sources as of 2026-08-15T06:32:42.880941+00:00.","reason":null,"source_receipts":[{"observed_at":"2026-08-15T06:32:42.880941+00:00","source":"crossref"},{"observed_at":"2026-08-15T06:32:39.529945+00:00","source":"retraction_watch"}],"state":"measured"}},{"citation":{"cited_paper":{"arxiv_id":"2311.09267","last_updated":"2023-11-15T08:59:06Z","snapshot_observed_at":"2026-08-13T05:24:58.711443Z","submitted_at":"2023-11-15T08:59:06Z","title":"Neuroscience inspired scientific machine learning (Part-1): Variable spiking neuron for regression","version":1},"cited_work":{"arxiv_id":"2311.09267","doi":null,"metadata_source":"pith","pith_arxiv_id":"2311.09267","snapshot_observed_at":"2026-08-11T17:11:04.864015Z","title":"Neuroscience inspired scientific machine learning (Part-1): Variable spiking neuron for regression","venue":"cs.NE","work_id":"eec07ce1-4972-4ff9-bf86-00687c7684cf","year":2023},"citing_paper":{"arxiv_id":"2412.09369","last_updated":"2024-12-12T15:37:02Z","snapshot_observed_at":"2026-08-12T01:38:56.032653Z","submitted_at":"2024-12-12T15:37:02Z","title":"Distribution free uncertainty quantification in neuroscience-inspired deep operators","version":1},"reference_index":43,"source":"pdf_text","source_observed_at":"2026-08-11T17:11:04.804918Z"},"links":{"cited_paper":"/paper/2311.09267","citing_paper":"/paper/2412.09369"},"observation_digest":"sha256:2d0289321712787acf290a0a509b1c705a2e4b31016088cfd21833a6581ef796","observation_id":"2b29fa30-10ce-450b-9fc8-0c9cb3323576","resolution":{"observed_at":"2026-08-11T17:11:04.871069Z","resolver_source":"local_arxiv","status":"verified_exact"},"standing_notice":{"events":[],"observation":"No event found in the named queried sources as of 2026-08-15T06:32:42.880941+00:00.","reason":null,"source_receipts":[{"observed_at":"2026-08-15T06:32:42.880941+00:00","source":"crossref"},{"observed_at":"2026-08-15T06:32:39.529945+00:00","source":"retraction_watch"}],"state":"measured"}},{"citation":{"cited_paper":null,"cited_work":{"arxiv_id":null,"doi":null,"metadata_source":"raw_reference","pith_arxiv_id":null,"snapshot_observed_at":"2026-08-11T17:11:20.136477Z","title":"Surrogate gradient learning in spiking neural net- works: Bringing the power of gradient-based optimization to spiking neural networks","venue":null,"work_id":"d6d03628-6207-4d2e-907e-5c5cd01e143f","year":2019},"citing_paper":{"arxiv_id":"2412.09369","last_updated":"2024-12-12T15:37:02Z","snapshot_observed_at":"2026-08-12T01:38:56.032653Z","submitted_at":"2024-12-12T15:37:02Z","title":"Distribution free uncertainty quantification in neuroscience-inspired deep operators","version":1},"reference_index":44,"source":"pdf_text","source_observed_at":"2026-08-11T17:11:04.811072Z"},"links":{"citing_paper":"/paper/2412.09369"},"observation_digest":"sha256:9d3ec8845deb0af07b035d5b4dd33604947946dd33e4e2fac9902298424013c8","observation_id":"781a6a5c-40d5-470f-9d12-18766a141267","resolution":{"observed_at":"2026-08-11T17:11:20.141817Z","resolver_source":"raw_fallback","status":"verified_fuzzy"},"standing_notice":{"events":[],"observation":"No event found in the named queried sources as of 2026-08-15T06:32:42.880941+00:00.","reason":null,"source_receipts":[{"observed_at":"2026-08-15T06:32:42.880941+00:00","source":"crossref"},{"observed_at":"2026-08-15T06:32:39.529945+00:00","source":"retraction_watch"}],"state":"measured"}},{"citation":{"cited_paper":null,"cited_work":{"arxiv_id":null,"doi":null,"metadata_source":"raw_reference","pith_arxiv_id":null,"snapshot_observed_at":"2026-08-11T17:11:20.112991Z","title":"Graph-theoretic-approach-assisted gaussian process for nonlinear stochastic dynamic analysis under generalized loading","venue":null,"work_id":"624a59b3-7504-49e6-9c4c-fbf6a3344fec","year":2019},"citing_paper":{"arxiv_id":"2412.09369","last_updated":"2024-12-12T15:37:02Z","snapshot_observed_at":"2026-08-12T01:38:56.032653Z","submitted_at":"2024-12-12T15:37:02Z","title":"Distribution free uncertainty quantification in neuroscience-inspired deep operators","version":1},"reference_index":45,"source":"pdf_text","source_observed_at":"2026-08-11T17:11:04.815235Z"},"links":{"citing_paper":"/paper/2412.09369"},"observation_digest":"sha256:2ab1e12dfad07b11ce09f86fb558a6fb7fd97c52d1c5d4c8821fdc4dae3e2ae6","observation_id":"73ebb18b-6eee-4d1c-a229-ec5ab246d56f","resolution":{"observed_at":"2026-08-11T17:11:20.118919Z","resolver_source":"raw_fallback","status":"verified_fuzzy"},"standing_notice":{"events":[],"observation":"No event found in the named queried sources as of 2026-08-15T06:32:42.880941+00:00.","reason":null,"source_receipts":[{"observed_at":"2026-08-15T06:32:42.880941+00:00","source":"crossref"},{"observed_at":"2026-08-15T06:32:39.529945+00:00","source":"retraction_watch"}],"state":"measured"}},{"citation":{"cited_paper":null,"cited_work":{"arxiv_id":null,"doi":null,"metadata_source":null,"pith_arxiv_id":null,"snapshot_observed_at":"2026-08-11T17:11:04.819546Z","title":"A comprehensive and fair comparison of two neural operators (with practical extensions) based on fair data","venue":null,"work_id":null,"year":2022},"citing_paper":{"arxiv_id":"2412.09369","last_updated":"2024-12-12T15:37:02Z","snapshot_observed_at":"2026-08-12T01:38:56.032653Z","submitted_at":"2024-12-12T15:37:02Z","title":"Distribution free uncertainty quantification in neuroscience-inspired deep operators","version":1},"reference_index":46,"source":"pdf_text","source_observed_at":"2026-08-11T17:11:04.819546Z"},"links":{"citing_paper":"/paper/2412.09369"},"observation_digest":"sha256:b7be9e510c7c5cfcd9121c3135dd8d1781b9f7681bbf28da893fe49430d7366f","observation_id":"ae2bcf2e-1163-4791-8a02-8dbd8dad328f","resolution":{"observed_at":"2026-08-11T17:11:04.819546Z","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":"raw_reference","pith_arxiv_id":null,"snapshot_observed_at":"2026-08-11T17:11:20.070598Z","title":"Openfwi: Large-scale multi-structural benchmark datasets for full waveform inversion","venue":null,"work_id":"37f4021e-0334-47b2-9908-0306a0f18aff","year":2022},"citing_paper":{"arxiv_id":"2412.09369","last_updated":"2024-12-12T15:37:02Z","snapshot_observed_at":"2026-08-12T01:38:56.032653Z","submitted_at":"2024-12-12T15:37:02Z","title":"Distribution free uncertainty quantification in neuroscience-inspired deep operators","version":1},"reference_index":47,"source":"pdf_text","source_observed_at":"2026-08-11T17:11:04.824145Z"},"links":{"citing_paper":"/paper/2412.09369"},"observation_digest":"sha256:e65440886d4ec823091685e974d6a336fed27e2fd69049006e19b49a45be30d6","observation_id":"6089b6ac-33de-4d51-b6e3-53dc4d3fa920","resolution":{"observed_at":"2026-08-11T17:11:20.078800Z","resolver_source":"raw_fallback","status":"malformed_identifier"},"standing_notice":{"events":[],"observation":"No event found in the named queried sources as of 2026-08-15T06:32:42.880941+00:00.","reason":null,"source_receipts":[{"observed_at":"2026-08-15T06:32:42.880941+00:00","source":"crossref"},{"observed_at":"2026-08-15T06:32:39.529945+00:00","source":"retraction_watch"}],"state":"measured"}}],"paper":{"arxiv_id":"2412.09369","last_updated":"2024-12-12T15:37:02Z","latest_version":1,"primary_category":"stat.ML","snapshot_observed_at":"2026-08-12T01:38:56.032653Z","submitted_at":"2024-12-12T15:37:02Z","title":"Distribution free uncertainty quantification in neuroscience-inspired deep operators"},"reference_resolution":{"displayed":47,"state_counts":{"malformed_identifier":1,"metadata_mismatch":0,"parse_uncertain":0,"unresolved":23,"verified_exact":3,"verified_fuzzy":20},"total_outbound_references":47},"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":"2026-08-15T06:32:42.880941+00:00","source":"crossref"},{"observed_at":"2026-08-15T06:32:39.529945+00:00","source":"retraction_watch"}],"thesis":"As of 16 August 2026, this Paper Citation Record lists 47 of 47 outbound references and 0 inbound Pith citation observations for arXiv:2412.09369."}