{"as_of":"2026-08-10T04:38:00Z","caps":{"database_statements":6,"inbound":100,"outbound":100},"context_digest":"sha256:6a15261697e33e04a1b8b8929cb9a9cada72c18f9c75a8e2d02bc6ac0d3c4505","coverage":[{"denominator":44,"lane":"reference_resolution","note":"Typed states for the displayed outbound observations.","records_observed":44,"source":"paper_references, paper_reference_links","source_observed_at":"2026-06-30T02:19:37.613971Z","state":"measured"},{"denominator":44,"lane":"standing_notices","note":"One-hop event checks from named stored sources.","records_observed":44,"source":"scholarly_work_events, retraction_status_cache","source_observed_at":"2026-08-09T06:31:02.800959+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/2606.29436/citation-record","integrity":"/paper/2606.29436/integrity","json":"/paper/2606.29436/citation-record.json","paper":"/paper/2606.29436"},"outbound":[{"citation":{"cited_paper":{"arxiv_id":"2411.18565","last_updated":"2026-08-02T10:10:40Z","snapshot_observed_at":"2026-08-06T23:22:58.558806Z","submitted_at":"2024-11-27T18:06:10Z","title":"A neural network approach to learning solutions of a class of elliptic variational inequalities","version":2},"cited_work":{"arxiv_id":"2411.18565","doi":null,"metadata_source":"arxiv_reference","pith_arxiv_id":"2411.18565","snapshot_observed_at":"2026-08-04T01:51:30.002067Z","title":"Alphonse, M","venue":null,"work_id":"d9a277e7-7481-4abb-b3d3-09381d2ab4ee","year":2024},"citing_paper":{"arxiv_id":"2606.29436","last_updated":"2026-06-28T14:53:37Z","snapshot_observed_at":"2026-08-01T18:34:21.596685Z","submitted_at":"2026-06-28T14:53:37Z","title":"Fourier Neural Operators with Least-Squares Readout Refit for Learning Random Obstacle-to-Solution Maps","version":1},"reference_index":1,"source":"pdf_text","source_observed_at":"2026-06-30T02:19:37.613971Z"},"links":{"cited_paper":"/paper/2411.18565","citing_paper":"/paper/2606.29436"},"observation_digest":"sha256:d7cd47a554af51ee6e1ad9cf6beb65b7eb5be8d2b013f1558fb0d22e57d1ac51","observation_id":"4a3697bb-b0fd-474c-9f8f-95a2846f0735","resolution":{"observed_at":"2026-08-04T01:51:30.002067Z","resolver_source":"arxiv_id","status":"verified_exact"},"standing_notice":{"events":[],"observation":"No event found in the named queried sources as of 2026-08-09T06:31:02.800959+00:00.","reason":null,"source_receipts":[{"observed_at":"2026-08-09T06:31:02.800959+00:00","source":"crossref"},{"observed_at":"2026-08-09T06:30:57.326959+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":"2026-06-30T02:19:37.613971Z","title":"Atkinson and W","venue":null,"work_id":null,"year":2009},"citing_paper":{"arxiv_id":"2606.29436","last_updated":"2026-06-28T14:53:37Z","snapshot_observed_at":"2026-08-01T18:34:21.596685Z","submitted_at":"2026-06-28T14:53:37Z","title":"Fourier Neural Operators with Least-Squares Readout Refit for Learning Random Obstacle-to-Solution Maps","version":1},"reference_index":2,"source":"pdf_text","source_observed_at":"2026-06-30T02:19:37.613971Z"},"links":{"citing_paper":"/paper/2606.29436"},"observation_digest":"sha256:bf83e81c1dd3d3f1f1ac65b9d7c49d4cdf982a1a8efe5d23dd083de733fcb4de","observation_id":"e5dfd728-6965-42d2-8fc4-62ef3ccae89e","resolution":{"observed_at":"2026-06-30T02:19:37.613971Z","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":"2026-06-30T02:19:37.613971Z","title":"Babuˇ ska and P","venue":null,"work_id":null,"year":2002},"citing_paper":{"arxiv_id":"2606.29436","last_updated":"2026-06-28T14:53:37Z","snapshot_observed_at":"2026-08-01T18:34:21.596685Z","submitted_at":"2026-06-28T14:53:37Z","title":"Fourier Neural Operators with Least-Squares Readout Refit for Learning Random Obstacle-to-Solution Maps","version":1},"reference_index":3,"source":"pdf_text","source_observed_at":"2026-06-30T02:19:37.613971Z"},"links":{"citing_paper":"/paper/2606.29436"},"observation_digest":"sha256:e568e776b02b8701ef4ec8ed478820a087c943375f717cbfdb242e2f984c79c1","observation_id":"27d49336-215d-4c01-a75e-72b5b7da4953","resolution":{"observed_at":"2026-06-30T02:19:37.613971Z","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":"2026-06-30T02:19:37.613971Z","title":"Babuˇ ska, R","venue":null,"work_id":null,"year":2004},"citing_paper":{"arxiv_id":"2606.29436","last_updated":"2026-06-28T14:53:37Z","snapshot_observed_at":"2026-08-01T18:34:21.596685Z","submitted_at":"2026-06-28T14:53:37Z","title":"Fourier Neural Operators with Least-Squares Readout Refit for Learning Random Obstacle-to-Solution Maps","version":1},"reference_index":4,"source":"pdf_text","source_observed_at":"2026-06-30T02:19:37.613971Z"},"links":{"citing_paper":"/paper/2606.29436"},"observation_digest":"sha256:4d94ecab224428098ba90a3c6cb77e42103fecdbefae4733f3f913312b2cbac0","observation_id":"166ebbb8-c2ac-4201-a5b0-9e2023067a4e","resolution":{"observed_at":"2026-06-30T02:19:37.613971Z","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":"2026-06-30T02:19:37.613971Z","title":"Babuˇ ska, F","venue":null,"work_id":null,"year":2007},"citing_paper":{"arxiv_id":"2606.29436","last_updated":"2026-06-28T14:53:37Z","snapshot_observed_at":"2026-08-01T18:34:21.596685Z","submitted_at":"2026-06-28T14:53:37Z","title":"Fourier Neural Operators with Least-Squares Readout Refit for Learning Random Obstacle-to-Solution Maps","version":1},"reference_index":5,"source":"pdf_text","source_observed_at":"2026-06-30T02:19:37.613971Z"},"links":{"citing_paper":"/paper/2606.29436"},"observation_digest":"sha256:e8b7e010a6b4cd4282393dd7daaaee7bc6d78a1ba41f9353c996514210b10d7b","observation_id":"4af62730-e9b0-4b27-a767-9732fd90c83d","resolution":{"observed_at":"2026-06-30T02:19:37.613971Z","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":"2026-06-30T02:19:37.613971Z","title":"El Bahja, J","venue":null,"work_id":null,"year":2025},"citing_paper":{"arxiv_id":"2606.29436","last_updated":"2026-06-28T14:53:37Z","snapshot_observed_at":"2026-08-01T18:34:21.596685Z","submitted_at":"2026-06-28T14:53:37Z","title":"Fourier Neural Operators with Least-Squares Readout Refit for Learning Random Obstacle-to-Solution Maps","version":1},"reference_index":6,"source":"pdf_text","source_observed_at":"2026-06-30T02:19:37.613971Z"},"links":{"citing_paper":"/paper/2606.29436"},"observation_digest":"sha256:7acf32bf4bda3231b72476f8cb806ff6ad6c3818324c43a57e37adbf39ecc48e","observation_id":"ae7ffeb3-59cc-4333-bd53-81f305c7421f","resolution":{"observed_at":"2026-06-30T02:19:37.613971Z","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":"2026-06-30T02:19:37.613971Z","title":"Bierig and A","venue":null,"work_id":null,"year":2015},"citing_paper":{"arxiv_id":"2606.29436","last_updated":"2026-06-28T14:53:37Z","snapshot_observed_at":"2026-08-01T18:34:21.596685Z","submitted_at":"2026-06-28T14:53:37Z","title":"Fourier Neural Operators with Least-Squares Readout Refit for Learning Random Obstacle-to-Solution Maps","version":1},"reference_index":7,"source":"pdf_text","source_observed_at":"2026-06-30T02:19:37.613971Z"},"links":{"citing_paper":"/paper/2606.29436"},"observation_digest":"sha256:e00383a372fcc0ee409a3c27c371cc68708f962cddac7005eea841c14b3ca487","observation_id":"1d96bcfa-d380-4d84-941e-080b2e84119c","resolution":{"observed_at":"2026-06-30T02:19:37.613971Z","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":"2026-06-30T02:19:37.613971Z","title":"Cheng, X","venue":null,"work_id":null,"year":2023},"citing_paper":{"arxiv_id":"2606.29436","last_updated":"2026-06-28T14:53:37Z","snapshot_observed_at":"2026-08-01T18:34:21.596685Z","submitted_at":"2026-06-28T14:53:37Z","title":"Fourier Neural Operators with Least-Squares Readout Refit for Learning Random Obstacle-to-Solution Maps","version":1},"reference_index":8,"source":"pdf_text","source_observed_at":"2026-06-30T02:19:37.613971Z"},"links":{"citing_paper":"/paper/2606.29436"},"observation_digest":"sha256:71eade2b6547a6595a7f99db44f25c77c90522044eda3035e6d4ee012f79cfc9","observation_id":"d6aebb6f-df3e-4aa7-808f-6efd843b6f33","resolution":{"observed_at":"2026-06-30T02:19:37.613971Z","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":"2026-06-30T02:19:37.613971Z","title":null,"venue":null,"work_id":null,"year":2020},"citing_paper":{"arxiv_id":"2606.29436","last_updated":"2026-06-28T14:53:37Z","snapshot_observed_at":"2026-08-01T18:34:21.596685Z","submitted_at":"2026-06-28T14:53:37Z","title":"Fourier Neural Operators with Least-Squares Readout Refit for Learning Random Obstacle-to-Solution Maps","version":1},"reference_index":9,"source":"pdf_text","source_observed_at":"2026-06-30T02:19:37.613971Z"},"links":{"citing_paper":"/paper/2606.29436"},"observation_digest":"sha256:ff1614d55267f14e0cdaac6876a4086af04a98ba6fffeb2b113286e9f0df2c05","observation_id":"83db66fa-276c-4b94-854e-b7c62154a440","resolution":{"observed_at":"2026-06-30T02:19:37.613971Z","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":"2026-06-30T02:19:37.613971Z","title":"Duvaut and J.-L","venue":null,"work_id":null,"year":1976},"citing_paper":{"arxiv_id":"2606.29436","last_updated":"2026-06-28T14:53:37Z","snapshot_observed_at":"2026-08-01T18:34:21.596685Z","submitted_at":"2026-06-28T14:53:37Z","title":"Fourier Neural Operators with Least-Squares Readout Refit for Learning Random Obstacle-to-Solution Maps","version":1},"reference_index":10,"source":"pdf_text","source_observed_at":"2026-06-30T02:19:37.613971Z"},"links":{"citing_paper":"/paper/2606.29436"},"observation_digest":"sha256:70d2d27672bc3d9b8fc589dade6be2a0e8160b5cd48f88f6af72e9eb0e1369a5","observation_id":"5d80670f-1f2d-477b-a487-e00623dcd339","resolution":{"observed_at":"2026-06-30T02:19:37.613971Z","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":"2026-06-30T02:19:37.613971Z","title":null,"venue":null,"work_id":null,"year":2005},"citing_paper":{"arxiv_id":"2606.29436","last_updated":"2026-06-28T14:53:37Z","snapshot_observed_at":"2026-08-01T18:34:21.596685Z","submitted_at":"2026-06-28T14:53:37Z","title":"Fourier Neural Operators with Least-Squares Readout Refit for Learning Random Obstacle-to-Solution Maps","version":1},"reference_index":11,"source":"pdf_text","source_observed_at":"2026-06-30T02:19:37.613971Z"},"links":{"citing_paper":"/paper/2606.29436"},"observation_digest":"sha256:2fe5821e50d19e099bd053e7286733f047fea084286d897aa6d0aaace5b6098d","observation_id":"abc5958f-420d-4d3f-9038-4d5fb8b30d41","resolution":{"observed_at":"2026-06-30T02:19:37.613971Z","resolver_source":null,"status":"unresolved"},"standing_notice":{"events":[],"reason":"canonical_work_link_unavailable","source_receipts":[],"state":"unavailable"}},{"citation":{"cited_paper":{"arxiv_id":"2504.05026","last_updated":"2025-04-07T12:50:56Z","snapshot_observed_at":"2026-08-07T16:07:59.280603Z","submitted_at":"2025-04-07T12:50:56Z","title":"Multi-level Neural Networks for high-dimensional parametric obstacle problems","version":1},"cited_work":{"arxiv_id":"2504.05026","doi":null,"metadata_source":"arxiv_reference","pith_arxiv_id":"2504.05026","snapshot_observed_at":"2026-06-30T02:24:13.302693Z","title":"Eigel, C","venue":null,"work_id":"68ed0815-e8e5-47c4-80eb-142f199c3a8b","year":2025},"citing_paper":{"arxiv_id":"2606.29436","last_updated":"2026-06-28T14:53:37Z","snapshot_observed_at":"2026-08-01T18:34:21.596685Z","submitted_at":"2026-06-28T14:53:37Z","title":"Fourier Neural Operators with Least-Squares Readout Refit for Learning Random Obstacle-to-Solution Maps","version":1},"reference_index":12,"source":"pdf_text","source_observed_at":"2026-06-30T02:19:37.613971Z"},"links":{"cited_paper":"/paper/2504.05026","citing_paper":"/paper/2606.29436"},"observation_digest":"sha256:221a0c9e022998e23ea7af59d3e2a98903f7d17917bd98fa6fed98fb168bda41","observation_id":"34d1a363-dd36-4e17-948b-4023aff5ed6e","resolution":{"observed_at":"2026-06-30T02:24:13.304676Z","resolver_source":"arxiv_id","status":"verified_exact"},"standing_notice":{"events":[],"observation":"No event found in the named queried sources as of 2026-08-09T06:31:02.800959+00:00.","reason":null,"source_receipts":[{"observed_at":"2026-08-09T06:31:02.800959+00:00","source":"crossref"},{"observed_at":"2026-08-09T06:30:57.326959+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":"2026-06-30T02:19:37.613971Z","title":"Forster and R","venue":null,"work_id":null,"year":2010},"citing_paper":{"arxiv_id":"2606.29436","last_updated":"2026-06-28T14:53:37Z","snapshot_observed_at":"2026-08-01T18:34:21.596685Z","submitted_at":"2026-06-28T14:53:37Z","title":"Fourier Neural Operators with Least-Squares Readout Refit for Learning Random Obstacle-to-Solution Maps","version":1},"reference_index":13,"source":"pdf_text","source_observed_at":"2026-06-30T02:19:37.613971Z"},"links":{"citing_paper":"/paper/2606.29436"},"observation_digest":"sha256:faa263a8483f77ccce15c0cda55188fd281fad9be8a18e936adcda0985bcd208","observation_id":"8594e6d3-10cf-4330-8f75-884b1a08f142","resolution":{"observed_at":"2026-06-30T02:19:37.613971Z","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":"2505.14430","doi":null,"metadata_source":"arxiv_reference","pith_arxiv_id":null,"snapshot_observed_at":"2026-06-30T02:24:13.306880Z","title":null,"venue":null,"work_id":"f8f00c48-3790-4fa7-8ef2-89773d265474","year":2025},"citing_paper":{"arxiv_id":"2606.29436","last_updated":"2026-06-28T14:53:37Z","snapshot_observed_at":"2026-08-01T18:34:21.596685Z","submitted_at":"2026-06-28T14:53:37Z","title":"Fourier Neural Operators with Least-Squares Readout Refit for Learning Random Obstacle-to-Solution Maps","version":1},"reference_index":14,"source":"pdf_text","source_observed_at":"2026-06-30T02:19:37.613971Z"},"links":{"citing_paper":"/paper/2606.29436"},"observation_digest":"sha256:06c28065e54f6d4289bb1339130115a0ad77cfac1b4545982d6be38c5c5d696f","observation_id":"de6c8d1d-93b9-41a8-b097-4f02dcdee85e","resolution":{"observed_at":"2026-06-30T02:24:13.308906Z","resolver_source":"arxiv_id","status":"verified_exact"},"standing_notice":{"events":[],"observation":"No event found in the named queried sources as of 2026-08-09T06:31:02.800959+00:00.","reason":null,"source_receipts":[{"observed_at":"2026-08-09T06:31:02.800959+00:00","source":"crossref"},{"observed_at":"2026-08-09T06:30:57.326959+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":"2026-06-30T02:19:37.613971Z","title":"Glowinski,Numerical Methods for Nonlinear Variational Problems, Springer-Verlag, New York, 1984","venue":null,"work_id":null,"year":1984},"citing_paper":{"arxiv_id":"2606.29436","last_updated":"2026-06-28T14:53:37Z","snapshot_observed_at":"2026-08-01T18:34:21.596685Z","submitted_at":"2026-06-28T14:53:37Z","title":"Fourier Neural Operators with Least-Squares Readout Refit for Learning Random Obstacle-to-Solution Maps","version":1},"reference_index":15,"source":"pdf_text","source_observed_at":"2026-06-30T02:19:37.613971Z"},"links":{"citing_paper":"/paper/2606.29436"},"observation_digest":"sha256:92e2832444ff2ef3a14bc964008237ea0849016aff023fa6ae8419138cfdc8ad","observation_id":"217e576f-a165-41df-88ee-910e6a8b458a","resolution":{"observed_at":"2026-06-30T02:19:37.613971Z","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":"2026-06-30T02:19:37.613971Z","title":"Glowinski, J.-L","venue":null,"work_id":null,"year":1981},"citing_paper":{"arxiv_id":"2606.29436","last_updated":"2026-06-28T14:53:37Z","snapshot_observed_at":"2026-08-01T18:34:21.596685Z","submitted_at":"2026-06-28T14:53:37Z","title":"Fourier Neural Operators with Least-Squares Readout Refit for Learning Random Obstacle-to-Solution Maps","version":1},"reference_index":16,"source":"pdf_text","source_observed_at":"2026-06-30T02:19:37.613971Z"},"links":{"citing_paper":"/paper/2606.29436"},"observation_digest":"sha256:53f7a411bc58f1af3682e56c3bc7b2f6655635ab9d0a146ba823347a18f8518f","observation_id":"a70b9690-309c-41b4-b5c4-7a50a0aa5d34","resolution":{"observed_at":"2026-06-30T02:19:37.613971Z","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":"2026-06-30T02:19:37.613971Z","title":"Han and B","venue":null,"work_id":null,"year":2013},"citing_paper":{"arxiv_id":"2606.29436","last_updated":"2026-06-28T14:53:37Z","snapshot_observed_at":"2026-08-01T18:34:21.596685Z","submitted_at":"2026-06-28T14:53:37Z","title":"Fourier Neural Operators with Least-Squares Readout Refit for Learning Random Obstacle-to-Solution Maps","version":1},"reference_index":17,"source":"pdf_text","source_observed_at":"2026-06-30T02:19:37.613971Z"},"links":{"citing_paper":"/paper/2606.29436"},"observation_digest":"sha256:073dad8052597be2da979010f69e1bf85b63e0107103a295b3e20321f2290345","observation_id":"2ce34a5b-4787-47dc-94c3-55b1d64929b2","resolution":{"observed_at":"2026-06-30T02:19:37.613971Z","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":"2026-06-30T02:19:37.613971Z","title":"Han and M","venue":null,"work_id":null,"year":2002},"citing_paper":{"arxiv_id":"2606.29436","last_updated":"2026-06-28T14:53:37Z","snapshot_observed_at":"2026-08-01T18:34:21.596685Z","submitted_at":"2026-06-28T14:53:37Z","title":"Fourier Neural Operators with Least-Squares Readout Refit for Learning Random Obstacle-to-Solution Maps","version":1},"reference_index":18,"source":"pdf_text","source_observed_at":"2026-06-30T02:19:37.613971Z"},"links":{"citing_paper":"/paper/2606.29436"},"observation_digest":"sha256:aa79eaa645ebf3a6f5b2f45fb09c87dbb430cbd6e80331666f3b33804e8f169a","observation_id":"ed6e1d35-6ba3-4bf1-bf3d-d42b37e9984f","resolution":{"observed_at":"2026-06-30T02:19:37.613971Z","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":"2026-06-30T02:19:37.613971Z","title":"Hlav´ aˇ cek, J","venue":null,"work_id":null,"year":1988},"citing_paper":{"arxiv_id":"2606.29436","last_updated":"2026-06-28T14:53:37Z","snapshot_observed_at":"2026-08-01T18:34:21.596685Z","submitted_at":"2026-06-28T14:53:37Z","title":"Fourier Neural Operators with Least-Squares Readout Refit for Learning Random Obstacle-to-Solution Maps","version":1},"reference_index":19,"source":"pdf_text","source_observed_at":"2026-06-30T02:19:37.613971Z"},"links":{"citing_paper":"/paper/2606.29436"},"observation_digest":"sha256:9b1161e6d59da8c87a0421d9fee4133caec8aba0e06c42645b67cc965454a7e7","observation_id":"2747b423-830e-4c44-ae57-2a7bce8dc414","resolution":{"observed_at":"2026-06-30T02:19:37.613971Z","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":"2026-06-30T02:19:37.613971Z","title":"H¨ ueber and B","venue":null,"work_id":null,"year":2005},"citing_paper":{"arxiv_id":"2606.29436","last_updated":"2026-06-28T14:53:37Z","snapshot_observed_at":"2026-08-01T18:34:21.596685Z","submitted_at":"2026-06-28T14:53:37Z","title":"Fourier Neural Operators with Least-Squares Readout Refit for Learning Random Obstacle-to-Solution Maps","version":1},"reference_index":20,"source":"pdf_text","source_observed_at":"2026-06-30T02:19:37.613971Z"},"links":{"citing_paper":"/paper/2606.29436"},"observation_digest":"sha256:6b4d5ea83b0a2b3742febbbc74d86cd2c42053b46f6d93824ffb1406767e5e55","observation_id":"2ce84863-055c-46b1-bbf7-94077edf3bb1","resolution":{"observed_at":"2026-06-30T02:19:37.613971Z","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":"2026-06-30T02:19:37.613971Z","title":"K¨ arkk¨ ainen, K","venue":null,"work_id":null,"year":2003},"citing_paper":{"arxiv_id":"2606.29436","last_updated":"2026-06-28T14:53:37Z","snapshot_observed_at":"2026-08-01T18:34:21.596685Z","submitted_at":"2026-06-28T14:53:37Z","title":"Fourier Neural Operators with Least-Squares Readout Refit for Learning Random Obstacle-to-Solution Maps","version":1},"reference_index":21,"source":"pdf_text","source_observed_at":"2026-06-30T02:19:37.613971Z"},"links":{"citing_paper":"/paper/2606.29436"},"observation_digest":"sha256:f9179be2354cd348e9087d16adb74445b3ab091f016e074e36552babc7d9c559","observation_id":"17657624-73be-453a-a474-0c1f6c20e631","resolution":{"observed_at":"2026-06-30T02:19:37.613971Z","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":"2026-06-30T02:19:37.613971Z","title":"Kikuchi and J","venue":null,"work_id":null,"year":1988},"citing_paper":{"arxiv_id":"2606.29436","last_updated":"2026-06-28T14:53:37Z","snapshot_observed_at":"2026-08-01T18:34:21.596685Z","submitted_at":"2026-06-28T14:53:37Z","title":"Fourier Neural Operators with Least-Squares Readout Refit for Learning Random Obstacle-to-Solution Maps","version":1},"reference_index":22,"source":"pdf_text","source_observed_at":"2026-06-30T02:19:37.613971Z"},"links":{"citing_paper":"/paper/2606.29436"},"observation_digest":"sha256:fb6ac2f29d97dd5da393a483968a4a768f4d84c2501742db7e40e3655c4725ef","observation_id":"62d1de6f-0087-452a-a81c-83f4e66b6db5","resolution":{"observed_at":"2026-06-30T02:19:37.613971Z","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":"2026-06-30T02:19:37.613971Z","title":"Kinderlehrer and G","venue":null,"work_id":null,"year":2000},"citing_paper":{"arxiv_id":"2606.29436","last_updated":"2026-06-28T14:53:37Z","snapshot_observed_at":"2026-08-01T18:34:21.596685Z","submitted_at":"2026-06-28T14:53:37Z","title":"Fourier Neural Operators with Least-Squares Readout Refit for Learning Random Obstacle-to-Solution Maps","version":1},"reference_index":23,"source":"pdf_text","source_observed_at":"2026-06-30T02:19:37.613971Z"},"links":{"citing_paper":"/paper/2606.29436"},"observation_digest":"sha256:7c84cafc81b1cd1e23de551a6ffbea446986618757185591f7e1a784f6ca52b6","observation_id":"aeb467c2-35c7-4ec5-96a7-ff899811927d","resolution":{"observed_at":"2026-06-30T02:19:37.613971Z","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":"2026-06-30T02:19:37.613971Z","title":"Kornhuber, C","venue":null,"work_id":null,"year":2014},"citing_paper":{"arxiv_id":"2606.29436","last_updated":"2026-06-28T14:53:37Z","snapshot_observed_at":"2026-08-01T18:34:21.596685Z","submitted_at":"2026-06-28T14:53:37Z","title":"Fourier Neural Operators with Least-Squares Readout Refit for Learning Random Obstacle-to-Solution Maps","version":1},"reference_index":24,"source":"pdf_text","source_observed_at":"2026-06-30T02:19:37.613971Z"},"links":{"citing_paper":"/paper/2606.29436"},"observation_digest":"sha256:3933aca872fdb9ec7f4772ccd4651dbbb705ab1aeab364fcbdff578d9837c669","observation_id":"06b1c2c5-9932-43b2-88d9-3f649b3f7902","resolution":{"observed_at":"2026-06-30T02:19:37.613971Z","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":"2026-06-30T02:19:37.613971Z","title":null,"venue":null,"work_id":null,"year":2021},"citing_paper":{"arxiv_id":"2606.29436","last_updated":"2026-06-28T14:53:37Z","snapshot_observed_at":"2026-08-01T18:34:21.596685Z","submitted_at":"2026-06-28T14:53:37Z","title":"Fourier Neural Operators with Least-Squares Readout Refit for Learning Random Obstacle-to-Solution Maps","version":1},"reference_index":25,"source":"pdf_text","source_observed_at":"2026-06-30T02:19:37.613971Z"},"links":{"citing_paper":"/paper/2606.29436"},"observation_digest":"sha256:e1eb41da439110f9af32ff73ea85f196340f622e28f8f519e321b3d324e5ca38","observation_id":"1e9fd7e3-f966-4ce1-bf2d-ba545c2cf2e2","resolution":{"observed_at":"2026-06-30T02:19:37.613971Z","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":"2026-06-30T02:19:37.613971Z","title":null,"venue":null,"work_id":null,"year":2023},"citing_paper":{"arxiv_id":"2606.29436","last_updated":"2026-06-28T14:53:37Z","snapshot_observed_at":"2026-08-01T18:34:21.596685Z","submitted_at":"2026-06-28T14:53:37Z","title":"Fourier Neural Operators with Least-Squares Readout Refit for Learning Random Obstacle-to-Solution Maps","version":1},"reference_index":26,"source":"pdf_text","source_observed_at":"2026-06-30T02:19:37.613971Z"},"links":{"citing_paper":"/paper/2606.29436"},"observation_digest":"sha256:eddbec3ccd59a5fa9e60c6d78d16f43d6aac0f23c5f3480b95b6ecd1f19250cb","observation_id":"cf4b40b5-72fa-49c0-8890-52d82a5e2ce5","resolution":{"observed_at":"2026-06-30T02:19:37.613971Z","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":"2026-06-30T02:19:37.613971Z","title":"Lanthaler and A","venue":null,"work_id":null,"year":2026},"citing_paper":{"arxiv_id":"2606.29436","last_updated":"2026-06-28T14:53:37Z","snapshot_observed_at":"2026-08-01T18:34:21.596685Z","submitted_at":"2026-06-28T14:53:37Z","title":"Fourier Neural Operators with Least-Squares Readout Refit for Learning Random Obstacle-to-Solution Maps","version":1},"reference_index":27,"source":"pdf_text","source_observed_at":"2026-06-30T02:19:37.613971Z"},"links":{"citing_paper":"/paper/2606.29436"},"observation_digest":"sha256:d6a07eff57fb1c9c4e043f39e12675fea04d140d3cee94a569d337d1fa72059f","observation_id":"2b3f2ecf-cc60-415c-af8f-b446dc4c69a2","resolution":{"observed_at":"2026-06-30T02:19:37.613971Z","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":"2026-06-30T02:19:37.613971Z","title":"Lee and Y","venue":null,"work_id":null,"year":2024},"citing_paper":{"arxiv_id":"2606.29436","last_updated":"2026-06-28T14:53:37Z","snapshot_observed_at":"2026-08-01T18:34:21.596685Z","submitted_at":"2026-06-28T14:53:37Z","title":"Fourier Neural Operators with Least-Squares Readout Refit for Learning Random Obstacle-to-Solution Maps","version":1},"reference_index":28,"source":"pdf_text","source_observed_at":"2026-06-30T02:19:37.613971Z"},"links":{"citing_paper":"/paper/2606.29436"},"observation_digest":"sha256:fd7ebc25087e633c5b93bb9b703554d5b95e482bc55da7f4c7f769e1d654f532","observation_id":"7aa50278-691d-4ac5-820a-e5f96c305057","resolution":{"observed_at":"2026-06-30T02:19:37.613971Z","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":"2026-06-30T02:19:37.613971Z","title":null,"venue":null,"work_id":null,"year":2021},"citing_paper":{"arxiv_id":"2606.29436","last_updated":"2026-06-28T14:53:37Z","snapshot_observed_at":"2026-08-01T18:34:21.596685Z","submitted_at":"2026-06-28T14:53:37Z","title":"Fourier Neural Operators with Least-Squares Readout Refit for Learning Random Obstacle-to-Solution Maps","version":1},"reference_index":29,"source":"pdf_text","source_observed_at":"2026-06-30T02:19:37.613971Z"},"links":{"citing_paper":"/paper/2606.29436"},"observation_digest":"sha256:95a86542b02aa8798d5f7d85d47b4766da7c9e501c7bcdf06d25664d23e8e7f3","observation_id":"a65407ab-7aa3-4fc9-a5a1-ce1bf7a886b0","resolution":{"observed_at":"2026-06-30T02:19:37.613971Z","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":"2026-06-30T02:19:37.613971Z","title":null,"venue":null,"work_id":null,"year":2021},"citing_paper":{"arxiv_id":"2606.29436","last_updated":"2026-06-28T14:53:37Z","snapshot_observed_at":"2026-08-01T18:34:21.596685Z","submitted_at":"2026-06-28T14:53:37Z","title":"Fourier Neural Operators with Least-Squares Readout Refit for Learning Random Obstacle-to-Solution Maps","version":1},"reference_index":30,"source":"pdf_text","source_observed_at":"2026-06-30T02:19:37.613971Z"},"links":{"citing_paper":"/paper/2606.29436"},"observation_digest":"sha256:0f753b31a2d75bc51b5d53983b8d43a54b395d606ed9f5920b5b69c4be428fe6","observation_id":"0101ee36-fca0-468e-8834-8adac0b6263e","resolution":{"observed_at":"2026-06-30T02:19:37.613971Z","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":"2026-06-30T02:19:37.613971Z","title":null,"venue":null,"work_id":null,"year":2022},"citing_paper":{"arxiv_id":"2606.29436","last_updated":"2026-06-28T14:53:37Z","snapshot_observed_at":"2026-08-01T18:34:21.596685Z","submitted_at":"2026-06-28T14:53:37Z","title":"Fourier Neural Operators with Least-Squares Readout Refit for Learning Random Obstacle-to-Solution Maps","version":1},"reference_index":31,"source":"pdf_text","source_observed_at":"2026-06-30T02:19:37.613971Z"},"links":{"citing_paper":"/paper/2606.29436"},"observation_digest":"sha256:c2e104644344b5793cedfeb6b9cec5a61253daf35271d8a1417eaac97046b2dd","observation_id":"22f4d099-5336-440e-b10d-629ef8c9f126","resolution":{"observed_at":"2026-06-30T02:19:37.613971Z","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":"2026-06-30T02:19:37.613971Z","title":null,"venue":null,"work_id":null,"year":2006},"citing_paper":{"arxiv_id":"2606.29436","last_updated":"2026-06-28T14:53:37Z","snapshot_observed_at":"2026-08-01T18:34:21.596685Z","submitted_at":"2026-06-28T14:53:37Z","title":"Fourier Neural Operators with Least-Squares Readout Refit for Learning Random Obstacle-to-Solution Maps","version":1},"reference_index":32,"source":"pdf_text","source_observed_at":"2026-06-30T02:19:37.613971Z"},"links":{"citing_paper":"/paper/2606.29436"},"observation_digest":"sha256:1def2d88b799a157364204a7445c674200f56d9d77eeeb7e01badc088b5f77a2","observation_id":"73176a2c-505f-4df6-b9b6-67f5caaa527d","resolution":{"observed_at":"2026-06-30T02:19:37.613971Z","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":"2026-06-30T02:19:37.613971Z","title":null,"venue":null,"work_id":null,"year":1987},"citing_paper":{"arxiv_id":"2606.29436","last_updated":"2026-06-28T14:53:37Z","snapshot_observed_at":"2026-08-01T18:34:21.596685Z","submitted_at":"2026-06-28T14:53:37Z","title":"Fourier Neural Operators with Least-Squares Readout Refit for Learning Random Obstacle-to-Solution Maps","version":1},"reference_index":33,"source":"pdf_text","source_observed_at":"2026-06-30T02:19:37.613971Z"},"links":{"citing_paper":"/paper/2606.29436"},"observation_digest":"sha256:9dc7fa50093bd943518a92d658e68f0623c348476bc1f31418e0021b7f358ec5","observation_id":"b6de0b09-a84d-4d28-8122-e7fe152fc6f4","resolution":{"observed_at":"2026-06-30T02:19:37.613971Z","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":"2026-06-30T02:19:37.613971Z","title":"Schwab and R","venue":null,"work_id":null,"year":2006},"citing_paper":{"arxiv_id":"2606.29436","last_updated":"2026-06-28T14:53:37Z","snapshot_observed_at":"2026-08-01T18:34:21.596685Z","submitted_at":"2026-06-28T14:53:37Z","title":"Fourier Neural Operators with Least-Squares Readout Refit for Learning Random Obstacle-to-Solution Maps","version":1},"reference_index":34,"source":"pdf_text","source_observed_at":"2026-06-30T02:19:37.613971Z"},"links":{"citing_paper":"/paper/2606.29436"},"observation_digest":"sha256:1d48682e4c281fbae3ae27bb725fd35cfc43555b01bbb1afc86d7fccfef4b79c","observation_id":"6a8b4a6e-a1c1-45d2-9556-4761f35f8d39","resolution":{"observed_at":"2026-06-30T02:19:37.613971Z","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":"2026-06-30T02:19:37.613971Z","title":"Schwab and A","venue":null,"work_id":null,"year":2022},"citing_paper":{"arxiv_id":"2606.29436","last_updated":"2026-06-28T14:53:37Z","snapshot_observed_at":"2026-08-01T18:34:21.596685Z","submitted_at":"2026-06-28T14:53:37Z","title":"Fourier Neural Operators with Least-Squares Readout Refit for Learning Random Obstacle-to-Solution Maps","version":1},"reference_index":35,"source":"pdf_text","source_observed_at":"2026-06-30T02:19:37.613971Z"},"links":{"citing_paper":"/paper/2606.29436"},"observation_digest":"sha256:73e667a11397d7782ae3593c0bfde4ea9f39631f86f87b56ffa82e9369eafb3e","observation_id":"4d946bb8-3c89-40fd-b85b-d2169b5cb0cc","resolution":{"observed_at":"2026-06-30T02:19:37.613971Z","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":"2026-06-30T02:19:37.613971Z","title":null,"venue":null,"work_id":null,"year":2007},"citing_paper":{"arxiv_id":"2606.29436","last_updated":"2026-06-28T14:53:37Z","snapshot_observed_at":"2026-08-01T18:34:21.596685Z","submitted_at":"2026-06-28T14:53:37Z","title":"Fourier Neural Operators with Least-Squares Readout Refit for Learning Random Obstacle-to-Solution Maps","version":1},"reference_index":36,"source":"pdf_text","source_observed_at":"2026-06-30T02:19:37.613971Z"},"links":{"citing_paper":"/paper/2606.29436"},"observation_digest":"sha256:b503beee21bd8c1c156847637851f78752c85a7d9684522229b836999c6637e7","observation_id":"b7f4158c-3119-41e7-8f68-cf2f4ef0db8c","resolution":{"observed_at":"2026-06-30T02:19:37.613971Z","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":"2026-06-30T02:19:37.613971Z","title":null,"venue":null,"work_id":null,"year":2021},"citing_paper":{"arxiv_id":"2606.29436","last_updated":"2026-06-28T14:53:37Z","snapshot_observed_at":"2026-08-01T18:34:21.596685Z","submitted_at":"2026-06-28T14:53:37Z","title":"Fourier Neural Operators with Least-Squares Readout Refit for Learning Random Obstacle-to-Solution Maps","version":1},"reference_index":37,"source":"pdf_text","source_observed_at":"2026-06-30T02:19:37.613971Z"},"links":{"citing_paper":"/paper/2606.29436"},"observation_digest":"sha256:6651cabd5eec385519fb3f374308e6512193a3ae267ea93820107a78ddecd7e5","observation_id":"b169e7a0-0fca-4d25-9252-eebd7173d32f","resolution":{"observed_at":"2026-06-30T02:19:37.613971Z","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":"2026-06-30T02:19:37.613971Z","title":null,"venue":null,"work_id":null,"year":2010},"citing_paper":{"arxiv_id":"2606.29436","last_updated":"2026-06-28T14:53:37Z","snapshot_observed_at":"2026-08-01T18:34:21.596685Z","submitted_at":"2026-06-28T14:53:37Z","title":"Fourier Neural Operators with Least-Squares Readout Refit for Learning Random Obstacle-to-Solution Maps","version":1},"reference_index":38,"source":"pdf_text","source_observed_at":"2026-06-30T02:19:37.613971Z"},"links":{"citing_paper":"/paper/2606.29436"},"observation_digest":"sha256:c7f448b825a048dea1f98b982da4ac9a3306938adccf5e83ae0e156c5eef0856","observation_id":"5c33c526-53a6-4aee-a1b7-7abc1b779aa8","resolution":{"observed_at":"2026-06-30T02:19:37.613971Z","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":"2026-06-30T02:19:37.613971Z","title":null,"venue":null,"work_id":null,"year":2014},"citing_paper":{"arxiv_id":"2606.29436","last_updated":"2026-06-28T14:53:37Z","snapshot_observed_at":"2026-08-01T18:34:21.596685Z","submitted_at":"2026-06-28T14:53:37Z","title":"Fourier Neural Operators with Least-Squares Readout Refit for Learning Random Obstacle-to-Solution Maps","version":1},"reference_index":39,"source":"pdf_text","source_observed_at":"2026-06-30T02:19:37.613971Z"},"links":{"citing_paper":"/paper/2606.29436"},"observation_digest":"sha256:0e76c59a450a7c5e5bea7bace2cfd562f8dc373cc6cc5053326b93c22fa47c65","observation_id":"d5dd55aa-8962-4fd3-9180-9066e1f7dc45","resolution":{"observed_at":"2026-06-30T02:19:37.613971Z","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":"2026-06-30T02:19:37.613971Z","title":"Wang and H","venue":null,"work_id":null,"year":2018},"citing_paper":{"arxiv_id":"2606.29436","last_updated":"2026-06-28T14:53:37Z","snapshot_observed_at":"2026-08-01T18:34:21.596685Z","submitted_at":"2026-06-28T14:53:37Z","title":"Fourier Neural Operators with Least-Squares Readout Refit for Learning Random Obstacle-to-Solution Maps","version":1},"reference_index":40,"source":"pdf_text","source_observed_at":"2026-06-30T02:19:37.613971Z"},"links":{"citing_paper":"/paper/2606.29436"},"observation_digest":"sha256:9a7244941116551c5ca90295bca6e32e087e257a4b6f52602fbd0f5fe963b2d4","observation_id":"279b79a0-40ca-4c90-9ff7-897290d9e9e8","resolution":{"observed_at":"2026-06-30T02:19:37.613971Z","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":"2026-06-30T02:19:37.613971Z","title":"Wang and H","venue":null,"work_id":null,"year":2020},"citing_paper":{"arxiv_id":"2606.29436","last_updated":"2026-06-28T14:53:37Z","snapshot_observed_at":"2026-08-01T18:34:21.596685Z","submitted_at":"2026-06-28T14:53:37Z","title":"Fourier Neural Operators with Least-Squares Readout Refit for Learning Random Obstacle-to-Solution Maps","version":1},"reference_index":41,"source":"pdf_text","source_observed_at":"2026-06-30T02:19:37.613971Z"},"links":{"citing_paper":"/paper/2606.29436"},"observation_digest":"sha256:44dfbb05296ba481c2121f9aab9a19245d77a3f295a8f295a00581394d227ba8","observation_id":"221fee4a-ce2d-4210-bd8c-977ecaec15f9","resolution":{"observed_at":"2026-06-30T02:19:37.613971Z","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":"2026-06-30T02:19:37.613971Z","title":"Xiu, Efficient collocational approach for parametric uncertainty analysis,Communi- cations in Computational Physics,2(2007), 293–309","venue":null,"work_id":null,"year":2007},"citing_paper":{"arxiv_id":"2606.29436","last_updated":"2026-06-28T14:53:37Z","snapshot_observed_at":"2026-08-01T18:34:21.596685Z","submitted_at":"2026-06-28T14:53:37Z","title":"Fourier Neural Operators with Least-Squares Readout Refit for Learning Random Obstacle-to-Solution Maps","version":1},"reference_index":42,"source":"pdf_text","source_observed_at":"2026-06-30T02:19:37.613971Z"},"links":{"citing_paper":"/paper/2606.29436"},"observation_digest":"sha256:1ed42ccdbd190738c22f00a195275d44bf3a85fd715a5acd2e5713815ae4e62a","observation_id":"eb3ca50a-ff69-4d2a-8ffc-edcb8de41cf9","resolution":{"observed_at":"2026-06-30T02:19:37.613971Z","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":"2026-06-30T02:19:37.613971Z","title":null,"venue":null,"work_id":null,"year":2022},"citing_paper":{"arxiv_id":"2606.29436","last_updated":"2026-06-28T14:53:37Z","snapshot_observed_at":"2026-08-01T18:34:21.596685Z","submitted_at":"2026-06-28T14:53:37Z","title":"Fourier Neural Operators with Least-Squares Readout Refit for Learning Random Obstacle-to-Solution Maps","version":1},"reference_index":43,"source":"pdf_text","source_observed_at":"2026-06-30T02:19:37.613971Z"},"links":{"citing_paper":"/paper/2606.29436"},"observation_digest":"sha256:e8d6a6c0fee232eb0a0dd796a1a588847b27a833ca30a6a3e36fa7f8d59d5b68","observation_id":"e0f146e2-aa4e-4027-a524-8f0e0e84039c","resolution":{"observed_at":"2026-06-30T02:19:37.613971Z","resolver_source":null,"status":"unresolved"},"standing_notice":{"events":[],"reason":"canonical_work_link_unavailable","source_receipts":[],"state":"unavailable"}},{"citation":{"cited_paper":{"arxiv_id":"2604.25111","last_updated":"2026-04-28T01:37:42Z","snapshot_observed_at":"2026-07-06T23:11:02.043135Z","submitted_at":"2026-04-28T01:37:42Z","title":"Numerical Analysis of Stochastic Elliptic Variational Inequalities of the First Kind","version":1},"cited_work":{"arxiv_id":"2604.25111","doi":null,"metadata_source":"pith","pith_arxiv_id":"2604.25111","snapshot_observed_at":"2026-06-30T02:24:13.310862Z","title":"Numerical Analysis of Stochastic Elliptic Variational Inequalities of the First Kind","venue":"math.NA","work_id":"a628f764-9c80-4462-997c-5d6d15340b79","year":2026},"citing_paper":{"arxiv_id":"2606.29436","last_updated":"2026-06-28T14:53:37Z","snapshot_observed_at":"2026-08-01T18:34:21.596685Z","submitted_at":"2026-06-28T14:53:37Z","title":"Fourier Neural Operators with Least-Squares Readout Refit for Learning Random Obstacle-to-Solution Maps","version":1},"reference_index":44,"source":"pdf_text","source_observed_at":"2026-06-30T02:19:37.613971Z"},"links":{"cited_paper":"/paper/2604.25111","citing_paper":"/paper/2606.29436"},"observation_digest":"sha256:c065fb57ad255bc39c3409027eae0877edc03ac67fee006ab245dc90c1c29ffa","observation_id":"b66f5480-8907-4a3c-a689-aa3194168e84","resolution":{"observed_at":"2026-06-30T02:24:13.312805Z","resolver_source":"local_arxiv","status":"verified_exact"},"standing_notice":{"events":[],"observation":"No event found in the named queried sources as of 2026-08-09T06:31:02.800959+00:00.","reason":null,"source_receipts":[{"observed_at":"2026-08-09T06:31:02.800959+00:00","source":"crossref"},{"observed_at":"2026-08-09T06:30:57.326959+00:00","source":"retraction_watch"}],"state":"measured"}}],"paper":{"arxiv_id":"2606.29436","last_updated":"2026-06-28T14:53:37Z","latest_version":1,"primary_category":"math.NA","snapshot_observed_at":"2026-08-01T18:34:21.596685Z","submitted_at":"2026-06-28T14:53:37Z","title":"Fourier Neural Operators with Least-Squares Readout Refit for Learning Random Obstacle-to-Solution Maps"},"reference_resolution":{"displayed":44,"state_counts":{"malformed_identifier":0,"metadata_mismatch":0,"parse_uncertain":0,"unresolved":40,"verified_exact":4,"verified_fuzzy":0},"total_outbound_references":44},"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":"2026-08-09T06:31:02.800959+00:00","source":"crossref"},{"observed_at":"2026-08-09T06:30:57.326959+00:00","source":"retraction_watch"}],"thesis":"As of 10 August 2026, this Paper Citation Record lists 44 of 44 outbound references and 0 inbound Pith citation observations for arXiv:2606.29436."}