{"as_of":"2026-08-10T14:35:00Z","caps":{"database_statements":6,"inbound":100,"outbound":100},"context_digest":"sha256:abdfcf4fbd587b635c10e93f6ac5ed9e2b9eab8dc2d2de5ad90ef71a55712ddd","coverage":[{"denominator":36,"lane":"reference_resolution","note":"Typed states for the displayed outbound observations.","records_observed":36,"source":"paper_references, paper_reference_links","source_observed_at":"2026-08-09T14:24:43.399654Z","state":"measured"},{"denominator":37,"lane":"standing_notices","note":"One-hop event checks from named stored sources.","records_observed":37,"source":"scholarly_work_events, retraction_status_cache","source_observed_at":"2026-08-10T06:31:04.303077+00:00","state":"measured"},{"denominator":1,"lane":"inbound_itemization","note":"Pith citing papers itemized under the disclosed page cap.","records_observed":1,"source":"paper_references, paper_reference_links","source_observed_at":"2026-05-18T14:24:48.666197Z","state":"measured"},{"denominator":1,"lane":"external_citation_measurements","note":"A source-named dated measurement, never combined with another source.","records_observed":0,"source":"arxiv_reference","source_observed_at":"2026-05-18T14:26:28.431385Z","state":"measured"}],"external_citation_measurements":[],"inbound":[{"citation":{"cited_paper":{"arxiv_id":"2502.01820","last_updated":"2025-02-03T20:54:37Z","snapshot_observed_at":"2026-08-10T11:53:01.884721Z","submitted_at":"2025-02-03T20:54:37Z","title":"Physics-Informed Surrogates for Temperature Prediction of Multi-Tracks in Laser Powder Bed Fusion","version":1},"cited_work":{"arxiv_id":"2502.01820","doi":null,"metadata_source":"arxiv_reference","pith_arxiv_id":"2502.01820","snapshot_observed_at":"2026-06-05T21:23:00.469572Z","title":null,"venue":null,"work_id":"c1ec9e6a-ce12-4b12-817d-f89f9b310819","year":null},"citing_paper":{"arxiv_id":"2509.21882","last_updated":"2026-05-25T20:11:55Z","snapshot_observed_at":"2026-08-04T14:57:15.491600Z","submitted_at":"2025-09-26T05:06:25Z","title":"Position: The Hidden Costs and Measurement Gaps of Reinforcement Learning with Verifiable Rewards","version":2},"reference_index":36,"source":"pdf_text","source_observed_at":"2026-05-18T14:24:48.666197Z"},"links":{"cited_paper":"/paper/2502.01820","citing_paper":"/paper/2509.21882"},"observation_digest":"sha256:e4fd58b7b46b383ecb8daade0d39163ebdc977e00c80e4caa24b210c0dbe0cda","observation_id":"5eae5c86-b72a-4fa6-b561-f72709a23191","resolution":{"observed_at":"2026-05-18T14:26:28.433536Z","resolver_source":"arxiv_id","status":"verified_exact"},"standing_notice":{"events":[],"observation":"No event found in the named queried sources as of 2026-08-10T06:31:04.303077+00:00.","reason":null,"source_receipts":[{"observed_at":"2026-08-10T06:31:04.303077+00:00","source":"crossref"},{"observed_at":"2026-08-10T06:30:57.382061+00:00","source":"retraction_watch"}],"state":"measured"}}],"links":{"evidence":"/evidence","html":"/paper/2502.01820/citation-record","integrity":"/paper/2502.01820/integrity","json":"/paper/2502.01820/citation-record.json","paper":"/paper/2502.01820"},"outbound":[{"citation":{"cited_paper":null,"cited_work":{"arxiv_id":null,"doi":null,"metadata_source":"raw_reference","pith_arxiv_id":null,"snapshot_observed_at":"2026-08-09T14:24:43.667946Z","title":"Parameter identification and uncertainty propagation of hydrogel coupled diffusion-deformation using pod- based reduced-order modeling","venue":null,"work_id":"fc9eb474-8017-473e-a47d-13b18f8fdbb2","year":2024},"citing_paper":{"arxiv_id":"2502.01820","last_updated":"2025-02-03T20:54:37Z","snapshot_observed_at":"2026-08-10T11:53:01.884721Z","submitted_at":"2025-02-03T20:54:37Z","title":"Physics-Informed Surrogates for Temperature Prediction of Multi-Tracks in Laser Powder Bed Fusion","version":1},"reference_index":1,"source":"pdf_text","source_observed_at":"2026-08-09T14:24:43.309623Z"},"links":{"citing_paper":"/paper/2502.01820"},"observation_digest":"sha256:9c81f63983efd2bc310f9228ffc9a018995aedab20784c57241161b4f0575fa0","observation_id":"ec104601-58d9-406e-8804-14efc2aa246d","resolution":{"observed_at":"2026-08-09T14:24:43.670720Z","resolver_source":"raw_fallback","status":"verified_fuzzy"},"standing_notice":{"events":[],"observation":"No event found in the named queried sources as of 2026-08-10T06:31:04.303077+00:00.","reason":null,"source_receipts":[{"observed_at":"2026-08-10T06:31:04.303077+00:00","source":"crossref"},{"observed_at":"2026-08-10T06:30:57.382061+00:00","source":"retraction_watch"}],"state":"measured"}},{"citation":{"cited_paper":{"arxiv_id":"2405.18311","last_updated":"2025-06-02T07:05:23Z","snapshot_observed_at":"2026-08-10T11:53:03.403203Z","submitted_at":"2024-05-28T16:02:11Z","title":"Deterministic and statistical calibration of constitutive models from full-field data with parametric physics-informed neural networks","version":3},"cited_work":{"arxiv_id":"2405.18311","doi":null,"metadata_source":"pith","pith_arxiv_id":"2405.18311","snapshot_observed_at":"2026-08-09T14:24:43.432561Z","title":"Deterministic and statistical calibration of constitutive models from full-field data with parametric physics-informed neural networks","venue":"cs.LG","work_id":"4f894162-0623-4966-ae95-50cd69274592","year":2024},"citing_paper":{"arxiv_id":"2502.01820","last_updated":"2025-02-03T20:54:37Z","snapshot_observed_at":"2026-08-10T11:53:01.884721Z","submitted_at":"2025-02-03T20:54:37Z","title":"Physics-Informed Surrogates for Temperature Prediction of Multi-Tracks in Laser Powder Bed Fusion","version":1},"reference_index":2,"source":"pdf_text","source_observed_at":"2026-08-09T14:24:43.312979Z"},"links":{"cited_paper":"/paper/2405.18311","citing_paper":"/paper/2502.01820"},"observation_digest":"sha256:190d1c753733ccec6b8b8ddec74ffc953d3dc4c57b4da564b3612db84c063940","observation_id":"3d3c8cc0-d196-4ac1-ae48-d0775d09b3f1","resolution":{"observed_at":"2026-08-09T14:24:43.437588Z","resolver_source":"local_arxiv","status":"verified_exact"},"standing_notice":{"events":[],"observation":"No event found in the named queried sources as of 2026-08-10T06:31:04.303077+00:00.","reason":null,"source_receipts":[{"observed_at":"2026-08-10T06:31:04.303077+00:00","source":"crossref"},{"observed_at":"2026-08-10T06:30:57.382061+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":"2026-08-09T14:24:43.660971Z","title":"Solving high-dimensional parametric engineering problems for inviscid flow around airfoils based on physics-informed neural networks","venue":null,"work_id":"a4cef233-d7ea-4b41-88c3-200c23b4a4b8","year":2024},"citing_paper":{"arxiv_id":"2502.01820","last_updated":"2025-02-03T20:54:37Z","snapshot_observed_at":"2026-08-10T11:53:01.884721Z","submitted_at":"2025-02-03T20:54:37Z","title":"Physics-Informed Surrogates for Temperature Prediction of Multi-Tracks in Laser Powder Bed Fusion","version":1},"reference_index":3,"source":"pdf_text","source_observed_at":"2026-08-09T14:24:43.316263Z"},"links":{"citing_paper":"/paper/2502.01820"},"observation_digest":"sha256:b5f7a976b72520808ca32cbba09de0a3e5e14290d6e891c73d18075b1468eb64","observation_id":"72b936c7-8641-4f43-9755-933e80a64f0e","resolution":{"observed_at":"2026-08-09T14:24:43.663600Z","resolver_source":"raw_fallback","status":"verified_fuzzy"},"standing_notice":{"events":[],"observation":"No event found in the named queried sources as of 2026-08-10T06:31:04.303077+00:00.","reason":null,"source_receipts":[{"observed_at":"2026-08-10T06:31:04.303077+00:00","source":"crossref"},{"observed_at":"2026-08-10T06:30:57.382061+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":"2026-08-09T14:24:43.654118Z","title":"Capturing local temperature evolution during additive manufacturing through fourier neural operators","venue":null,"work_id":"069f26f3-df78-4ac0-93f4-b0e8edb4b332","year":2024},"citing_paper":{"arxiv_id":"2502.01820","last_updated":"2025-02-03T20:54:37Z","snapshot_observed_at":"2026-08-10T11:53:01.884721Z","submitted_at":"2025-02-03T20:54:37Z","title":"Physics-Informed Surrogates for Temperature Prediction of Multi-Tracks in Laser Powder Bed Fusion","version":1},"reference_index":4,"source":"pdf_text","source_observed_at":"2026-08-09T14:24:43.319174Z"},"links":{"citing_paper":"/paper/2502.01820"},"observation_digest":"sha256:8f5749976c7b7e0ecd1c5cfbbeebe3b22fe5b9e209dd9653ace52076ea970b51","observation_id":"726a917b-6975-4b7e-98ff-00a72ea0e080","resolution":{"observed_at":"2026-08-09T14:24:43.656743Z","resolver_source":"raw_fallback","status":"verified_fuzzy"},"standing_notice":{"events":[],"observation":"No event found in the named queried sources as of 2026-08-10T06:31:04.303077+00:00.","reason":null,"source_receipts":[{"observed_at":"2026-08-10T06:31:04.303077+00:00","source":"crossref"},{"observed_at":"2026-08-10T06:30:57.382061+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":"2026-08-09T14:24:43.646994Z","title":"A deeponet multi-fidelity approach for residual learning in reduced order modeling","venue":null,"work_id":"cbfd7b37-0d67-41f6-990b-c449e2ec5169","year":2023},"citing_paper":{"arxiv_id":"2502.01820","last_updated":"2025-02-03T20:54:37Z","snapshot_observed_at":"2026-08-10T11:53:01.884721Z","submitted_at":"2025-02-03T20:54:37Z","title":"Physics-Informed Surrogates for Temperature Prediction of Multi-Tracks in Laser Powder Bed Fusion","version":1},"reference_index":5,"source":"pdf_text","source_observed_at":"2026-08-09T14:24:43.321766Z"},"links":{"citing_paper":"/paper/2502.01820"},"observation_digest":"sha256:b24c670dfd4e4d1dfebc0c8158ea6432ce77ed26520605fecbad8ba33940b952","observation_id":"2633a576-c40d-4414-9c12-468fd89a6cc0","resolution":{"observed_at":"2026-08-09T14:24:43.649859Z","resolver_source":"raw_fallback","status":"verified_fuzzy"},"standing_notice":{"events":[],"observation":"No event found in the named queried sources as of 2026-08-10T06:31:04.303077+00:00.","reason":null,"source_receipts":[{"observed_at":"2026-08-10T06:31:04.303077+00:00","source":"crossref"},{"observed_at":"2026-08-10T06:30:57.382061+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":"2026-08-09T14:24:43.639895Z","title":"Reduced order modeling via pgd for highly transient thermal evolutions in additive manufacturing","venue":null,"work_id":"f33228b0-96c4-4aaf-8b5b-3f0a9887fa24","year":2019},"citing_paper":{"arxiv_id":"2502.01820","last_updated":"2025-02-03T20:54:37Z","snapshot_observed_at":"2026-08-10T11:53:01.884721Z","submitted_at":"2025-02-03T20:54:37Z","title":"Physics-Informed Surrogates for Temperature Prediction of Multi-Tracks in Laser Powder Bed Fusion","version":1},"reference_index":6,"source":"pdf_text","source_observed_at":"2026-08-09T14:24:43.324537Z"},"links":{"citing_paper":"/paper/2502.01820"},"observation_digest":"sha256:4298ed323ede47157a4949f781bd6fc259fc77b5a0a3fbc751e9166340d68fc7","observation_id":"33b87b9a-8dac-467f-b84e-1c30926871cf","resolution":{"observed_at":"2026-08-09T14:24:43.642650Z","resolver_source":"raw_fallback","status":"verified_fuzzy"},"standing_notice":{"events":[],"observation":"No event found in the named queried sources as of 2026-08-10T06:31:04.303077+00:00.","reason":null,"source_receipts":[{"observed_at":"2026-08-10T06:31:04.303077+00:00","source":"crossref"},{"observed_at":"2026-08-10T06:30:57.382061+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":"2026-08-09T14:24:43.632540Z","title":"Toolpath generation for the manufacture of metallic components by means of the laser metal deposition technique","venue":null,"work_id":"3a70af06-8100-4281-94de-e108365f8dcb","year":2019},"citing_paper":{"arxiv_id":"2502.01820","last_updated":"2025-02-03T20:54:37Z","snapshot_observed_at":"2026-08-10T11:53:01.884721Z","submitted_at":"2025-02-03T20:54:37Z","title":"Physics-Informed Surrogates for Temperature Prediction of Multi-Tracks in Laser Powder Bed Fusion","version":1},"reference_index":7,"source":"pdf_text","source_observed_at":"2026-08-09T14:24:43.327438Z"},"links":{"citing_paper":"/paper/2502.01820"},"observation_digest":"sha256:0b51dcfebaee0d0a49d33312b1e8ee219083a5cc079c2e76cf6badce60611c6d","observation_id":"e236b342-b2ee-4a51-ac90-85bcd83bcbcb","resolution":{"observed_at":"2026-08-09T14:24:43.635306Z","resolver_source":"raw_fallback","status":"verified_fuzzy"},"standing_notice":{"events":[],"observation":"No event found in the named queried sources as of 2026-08-10T06:31:04.303077+00:00.","reason":null,"source_receipts":[{"observed_at":"2026-08-10T06:31:04.303077+00:00","source":"crossref"},{"observed_at":"2026-08-10T06:30:57.382061+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":"2026-08-09T14:24:43.625128Z","title":"On ther- mal modeling of additive manufacturing processes","venue":null,"work_id":"d369eac0-cb34-4fb6-ae70-1ed9bbf952a5","year":2018},"citing_paper":{"arxiv_id":"2502.01820","last_updated":"2025-02-03T20:54:37Z","snapshot_observed_at":"2026-08-10T11:53:01.884721Z","submitted_at":"2025-02-03T20:54:37Z","title":"Physics-Informed Surrogates for Temperature Prediction of Multi-Tracks in Laser Powder Bed Fusion","version":1},"reference_index":8,"source":"pdf_text","source_observed_at":"2026-08-09T14:24:43.329925Z"},"links":{"citing_paper":"/paper/2502.01820"},"observation_digest":"sha256:5929b37453eaa88a0e9bceed8823fcfe97fa7b78f805757c326fd14b91390743","observation_id":"775ccc10-2b90-4300-ba69-65b1de447f07","resolution":{"observed_at":"2026-08-09T14:24:43.627866Z","resolver_source":"raw_fallback","status":"verified_fuzzy"},"standing_notice":{"events":[],"observation":"No event found in the named queried sources as of 2026-08-10T06:31:04.303077+00:00.","reason":null,"source_receipts":[{"observed_at":"2026-08-10T06:31:04.303077+00:00","source":"crossref"},{"observed_at":"2026-08-10T06:30:57.382061+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":"2026-08-09T14:24:43.617761Z","title":"A comprehensive deep learning-based approach to reduced order modeling of nonlinear time-dependent parametrized pdes","venue":null,"work_id":"79b26a23-a574-4fe4-a01f-d80b5df4c7c2","year":2021},"citing_paper":{"arxiv_id":"2502.01820","last_updated":"2025-02-03T20:54:37Z","snapshot_observed_at":"2026-08-10T11:53:01.884721Z","submitted_at":"2025-02-03T20:54:37Z","title":"Physics-Informed Surrogates for Temperature Prediction of Multi-Tracks in Laser Powder Bed Fusion","version":1},"reference_index":9,"source":"pdf_text","source_observed_at":"2026-08-09T14:24:43.332273Z"},"links":{"citing_paper":"/paper/2502.01820"},"observation_digest":"sha256:e1e25476ee419d998fb0b61413d50fb5af2f6b254879ffce9aa432267321b925","observation_id":"11b0b192-0ba1-4a78-b909-36a858ee19c9","resolution":{"observed_at":"2026-08-09T14:24:43.620613Z","resolver_source":"raw_fallback","status":"verified_fuzzy"},"standing_notice":{"events":[],"observation":"No event found in the named queried sources as of 2026-08-10T06:31:04.303077+00:00.","reason":null,"source_receipts":[{"observed_at":"2026-08-10T06:31:04.303077+00:00","source":"crossref"},{"observed_at":"2026-08-10T06:30:57.382061+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":"2026-08-09T14:24:43.610513Z","title":"En-deeponet: An enrichment ap- proach for enhancing the expressivity of neural operators with applications to seismology","venue":null,"work_id":"f112c487-fa8d-4674-b0d4-aa9a24365979","year":2024},"citing_paper":{"arxiv_id":"2502.01820","last_updated":"2025-02-03T20:54:37Z","snapshot_observed_at":"2026-08-10T11:53:01.884721Z","submitted_at":"2025-02-03T20:54:37Z","title":"Physics-Informed Surrogates for Temperature Prediction of Multi-Tracks in Laser Powder Bed Fusion","version":1},"reference_index":10,"source":"pdf_text","source_observed_at":"2026-08-09T14:24:43.334849Z"},"links":{"citing_paper":"/paper/2502.01820"},"observation_digest":"sha256:d5d3e3bf4dd2ef7783c51d10c02493c17b71ec3b776b42fd2f13c5c271fe706f","observation_id":"3f90e79c-4330-45b6-a9ae-bd31396cb076","resolution":{"observed_at":"2026-08-09T14:24:43.613255Z","resolver_source":"raw_fallback","status":"verified_fuzzy"},"standing_notice":{"events":[],"observation":"No event found in the named queried sources as of 2026-08-10T06:31:04.303077+00:00.","reason":null,"source_receipts":[{"observed_at":"2026-08-10T06:31:04.303077+00:00","source":"crossref"},{"observed_at":"2026-08-10T06:30:57.382061+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":"2026-08-09T14:24:43.602694Z","title":"Single-track thermal analysis of laser powder bed fusion process: Parametric solution through physics-informed neural networks","venue":null,"work_id":"427085cc-fe71-4161-8105-eb451e2684b5","year":2023},"citing_paper":{"arxiv_id":"2502.01820","last_updated":"2025-02-03T20:54:37Z","snapshot_observed_at":"2026-08-10T11:53:01.884721Z","submitted_at":"2025-02-03T20:54:37Z","title":"Physics-Informed Surrogates for Temperature Prediction of Multi-Tracks in Laser Powder Bed Fusion","version":1},"reference_index":11,"source":"pdf_text","source_observed_at":"2026-08-09T14:24:43.337324Z"},"links":{"citing_paper":"/paper/2502.01820"},"observation_digest":"sha256:6bac63ec56d805c373344da73c8c201bfe9ad4b90e78dc9055d0b1f7597761b7","observation_id":"b1fafb48-8663-4f08-993a-e45b5c3518d7","resolution":{"observed_at":"2026-08-09T14:24:43.605689Z","resolver_source":"raw_fallback","status":"verified_fuzzy"},"standing_notice":{"events":[],"observation":"No event found in the named queried sources as of 2026-08-10T06:31:04.303077+00:00.","reason":null,"source_receipts":[{"observed_at":"2026-08-10T06:31:04.303077+00:00","source":"crossref"},{"observed_at":"2026-08-10T06:30:57.382061+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":"2026-08-09T14:24:43.594534Z","title":"Enhancing pinns for solving pdes via adaptive collocation point movement and adaptive loss weighting","venue":null,"work_id":"b8aee1c8-231e-4a4e-a366-99db0152b748","year":2023},"citing_paper":{"arxiv_id":"2502.01820","last_updated":"2025-02-03T20:54:37Z","snapshot_observed_at":"2026-08-10T11:53:01.884721Z","submitted_at":"2025-02-03T20:54:37Z","title":"Physics-Informed Surrogates for Temperature Prediction of Multi-Tracks in Laser Powder Bed Fusion","version":1},"reference_index":12,"source":"pdf_text","source_observed_at":"2026-08-09T14:24:43.340015Z"},"links":{"citing_paper":"/paper/2502.01820"},"observation_digest":"sha256:4222222e87bb200cfe6798210d83ae6e5192c77ac2aa728080e840a1be3b87e2","observation_id":"08230cf0-b72f-4769-9a9a-b90b87d5d72b","resolution":{"observed_at":"2026-08-09T14:24:43.597565Z","resolver_source":"raw_fallback","status":"verified_fuzzy"},"standing_notice":{"events":[],"observation":"No event found in the named queried sources as of 2026-08-10T06:31:04.303077+00:00.","reason":null,"source_receipts":[{"observed_at":"2026-08-10T06:31:04.303077+00:00","source":"crossref"},{"observed_at":"2026-08-10T06:30:57.382061+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":"2026-08-09T14:24:43.586941Z","title":"Tool path optimization of selective laser sintering processes using deep learning","venue":null,"work_id":"31b0a25d-87bb-4473-b24e-ae66f82155c7","year":2022},"citing_paper":{"arxiv_id":"2502.01820","last_updated":"2025-02-03T20:54:37Z","snapshot_observed_at":"2026-08-10T11:53:01.884721Z","submitted_at":"2025-02-03T20:54:37Z","title":"Physics-Informed Surrogates for Temperature Prediction of Multi-Tracks in Laser Powder Bed Fusion","version":1},"reference_index":13,"source":"pdf_text","source_observed_at":"2026-08-09T14:24:43.342412Z"},"links":{"citing_paper":"/paper/2502.01820"},"observation_digest":"sha256:6bcb390cf1dfd3652d1d69dfb3038e0ccd796c09c98aecab5dd7c41a0f96e3ea","observation_id":"0d8d28f5-7e2d-4c3f-8cde-6f5e0d86cb08","resolution":{"observed_at":"2026-08-09T14:24:43.589894Z","resolver_source":"raw_fallback","status":"verified_fuzzy"},"standing_notice":{"events":[],"observation":"No event found in the named queried sources as of 2026-08-10T06:31:04.303077+00:00.","reason":null,"source_receipts":[{"observed_at":"2026-08-10T06:31:04.303077+00:00","source":"crossref"},{"observed_at":"2026-08-10T06:30:57.382061+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":"2026-08-09T14:24:43.579328Z","title":"An efficient and high- fidelity local multi-mesh finite volume method for heat transfer and fluid flow problems in metal additive manufacturing","venue":null,"work_id":"7e08ad12-5f91-4d61-be8f-f04cd86e7e1a","year":2023},"citing_paper":{"arxiv_id":"2502.01820","last_updated":"2025-02-03T20:54:37Z","snapshot_observed_at":"2026-08-10T11:53:01.884721Z","submitted_at":"2025-02-03T20:54:37Z","title":"Physics-Informed Surrogates for Temperature Prediction of Multi-Tracks in Laser Powder Bed Fusion","version":1},"reference_index":14,"source":"pdf_text","source_observed_at":"2026-08-09T14:24:43.344912Z"},"links":{"citing_paper":"/paper/2502.01820"},"observation_digest":"sha256:8d703fe2dd6a877e33ce513db486b4be89531e8b5af34a319030b36a303bf33d","observation_id":"e7049b99-1f57-48ca-bca9-6f7b527590d0","resolution":{"observed_at":"2026-08-09T14:24:43.582220Z","resolver_source":"raw_fallback","status":"verified_fuzzy"},"standing_notice":{"events":[],"observation":"No event found in the named queried sources as of 2026-08-10T06:31:04.303077+00:00.","reason":null,"source_receipts":[{"observed_at":"2026-08-10T06:31:04.303077+00:00","source":"crossref"},{"observed_at":"2026-08-10T06:30:57.382061+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":"2026-08-09T14:24:43.571689Z","title":"A physics- informed neural network framework to predict 3d temperature field without labeled data in process of laser metal deposition","venue":null,"work_id":"6d344e50-e7d9-40f1-be6e-fa7e09d72899","year":2023},"citing_paper":{"arxiv_id":"2502.01820","last_updated":"2025-02-03T20:54:37Z","snapshot_observed_at":"2026-08-10T11:53:01.884721Z","submitted_at":"2025-02-03T20:54:37Z","title":"Physics-Informed Surrogates for Temperature Prediction of Multi-Tracks in Laser Powder Bed Fusion","version":1},"reference_index":15,"source":"pdf_text","source_observed_at":"2026-08-09T14:24:43.347326Z"},"links":{"citing_paper":"/paper/2502.01820"},"observation_digest":"sha256:06b09bf7765d3bd22e72086bdb82506def94d6b857cf2a868bdd5ff25ba82e1c","observation_id":"146eea3b-2a44-4209-bc6c-df686a751625","resolution":{"observed_at":"2026-08-09T14:24:43.574630Z","resolver_source":"raw_fallback","status":"verified_fuzzy"},"standing_notice":{"events":[],"observation":"No event found in the named queried sources as of 2026-08-10T06:31:04.303077+00:00.","reason":null,"source_receipts":[{"observed_at":"2026-08-10T06:31:04.303077+00:00","source":"crossref"},{"observed_at":"2026-08-10T06:30:57.382061+00:00","source":"retraction_watch"}],"state":"measured"}},{"citation":{"cited_paper":{"arxiv_id":"2010.08895","last_updated":"2021-05-17T03:12:33Z","snapshot_observed_at":"2026-07-06T10:05:26.653366Z","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":"2026-08-09T14:24:43.349803Z","title":"Fourier neural operator for parametric partial differential equations","venue":null,"work_id":null,"year":2010},"citing_paper":{"arxiv_id":"2502.01820","last_updated":"2025-02-03T20:54:37Z","snapshot_observed_at":"2026-08-10T11:53:01.884721Z","submitted_at":"2025-02-03T20:54:37Z","title":"Physics-Informed Surrogates for Temperature Prediction of Multi-Tracks in Laser Powder Bed Fusion","version":1},"reference_index":16,"source":"pdf_text","source_observed_at":"2026-08-09T14:24:43.349803Z"},"links":{"cited_paper":"/paper/2010.08895","citing_paper":"/paper/2502.01820"},"observation_digest":"sha256:8fb6f72504078e798d393af7117cb1b3aaa422d6d8674c232a6f34d3f347951b","observation_id":"7e8ce358-72cf-44e1-9d8d-530270f0063c","resolution":{"observed_at":"2026-08-09T14:24:43.349803Z","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":"2026-08-09T14:24:43.563741Z","title":"Hy- brid thermal modeling of additive manufacturing processes using physics-informed neural networks for temperature prediction and parameter identification","venue":null,"work_id":"99d2af2a-08c9-4c38-9f10-13bbbfc63f5b","year":2023},"citing_paper":{"arxiv_id":"2502.01820","last_updated":"2025-02-03T20:54:37Z","snapshot_observed_at":"2026-08-10T11:53:01.884721Z","submitted_at":"2025-02-03T20:54:37Z","title":"Physics-Informed Surrogates for Temperature Prediction of Multi-Tracks in Laser Powder Bed Fusion","version":1},"reference_index":17,"source":"pdf_text","source_observed_at":"2026-08-09T14:24:43.352699Z"},"links":{"citing_paper":"/paper/2502.01820"},"observation_digest":"sha256:4f923ea42d5ca4042f8985f3882d8a7a24ddd0aa84d1c103220ea28f72edad09","observation_id":"6e95a35f-7e74-4283-92d3-7056da874b45","resolution":{"observed_at":"2026-08-09T14:24:43.566705Z","resolver_source":"raw_fallback","status":"verified_fuzzy"},"standing_notice":{"events":[],"observation":"No event found in the named queried sources as of 2026-08-10T06:31:04.303077+00:00.","reason":null,"source_receipts":[{"observed_at":"2026-08-10T06:31:04.303077+00:00","source":"crossref"},{"observed_at":"2026-08-10T06:30:57.382061+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":"2026-08-09T14:24:43.355135Z","title":"On the limited memory bfgs method for large scale optimization","venue":null,"work_id":null,"year":1989},"citing_paper":{"arxiv_id":"2502.01820","last_updated":"2025-02-03T20:54:37Z","snapshot_observed_at":"2026-08-10T11:53:01.884721Z","submitted_at":"2025-02-03T20:54:37Z","title":"Physics-Informed Surrogates for Temperature Prediction of Multi-Tracks in Laser Powder Bed Fusion","version":1},"reference_index":18,"source":"pdf_text","source_observed_at":"2026-08-09T14:24:43.355135Z"},"links":{"citing_paper":"/paper/2502.01820"},"observation_digest":"sha256:071f428cbfaccfa193ff86c80329b3d58964543f895006a189c69df58b943166","observation_id":"3b731018-fc29-42ce-9229-4dc09071a5a2","resolution":{"observed_at":"2026-08-09T14:24:43.355135Z","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":"2026-08-09T14:24:43.357563Z","title":"Learning nonlinear operators via deeponet based on the universal approximation theorem of operators.Nature machine intelligence, 3(3):218–229, 2021","venue":null,"work_id":null,"year":2021},"citing_paper":{"arxiv_id":"2502.01820","last_updated":"2025-02-03T20:54:37Z","snapshot_observed_at":"2026-08-10T11:53:01.884721Z","submitted_at":"2025-02-03T20:54:37Z","title":"Physics-Informed Surrogates for Temperature Prediction of Multi-Tracks in Laser Powder Bed Fusion","version":1},"reference_index":19,"source":"pdf_text","source_observed_at":"2026-08-09T14:24:43.357563Z"},"links":{"citing_paper":"/paper/2502.01820"},"observation_digest":"sha256:a9488cdf040ddc631d3b5f646920ccbf93c349d3d992ad686ae9755bcc5f422f","observation_id":"104f1b5a-7815-4a6b-90b0-f66e8bde1b1e","resolution":{"observed_at":"2026-08-09T14:24:43.357563Z","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":"2026-08-09T14:24:43.359968Z","title":"Physics-informed neural networks for high-speed flows","venue":null,"work_id":null,"year":2020},"citing_paper":{"arxiv_id":"2502.01820","last_updated":"2025-02-03T20:54:37Z","snapshot_observed_at":"2026-08-10T11:53:01.884721Z","submitted_at":"2025-02-03T20:54:37Z","title":"Physics-Informed Surrogates for Temperature Prediction of Multi-Tracks in Laser Powder Bed Fusion","version":1},"reference_index":20,"source":"pdf_text","source_observed_at":"2026-08-09T14:24:43.359968Z"},"links":{"citing_paper":"/paper/2502.01820"},"observation_digest":"sha256:4fe3ac85403296337f99fce9c84abe79878016821d6fc85312dee3a1d6e18f4c","observation_id":"278cbe21-c45b-4d0c-8530-4924ed29c656","resolution":{"observed_at":"2026-08-09T14:24:43.359968Z","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":"2026-08-09T14:24:43.544357Z","title":"Multiscale modeling of powder bed–based additive manufac- turing","venue":null,"work_id":"e777e186-6a8b-4472-ae58-91029f0177aa","year":2016},"citing_paper":{"arxiv_id":"2502.01820","last_updated":"2025-02-03T20:54:37Z","snapshot_observed_at":"2026-08-10T11:53:01.884721Z","submitted_at":"2025-02-03T20:54:37Z","title":"Physics-Informed Surrogates for Temperature Prediction of Multi-Tracks in Laser Powder Bed Fusion","version":1},"reference_index":21,"source":"pdf_text","source_observed_at":"2026-08-09T14:24:43.362348Z"},"links":{"citing_paper":"/paper/2502.01820"},"observation_digest":"sha256:ee5373c8b7186e0de84d049316eb04cb998ae2643b2656849f6321396b95cdef","observation_id":"cd9a420a-b460-4073-b243-97deaa207caa","resolution":{"observed_at":"2026-08-09T14:24:43.547111Z","resolver_source":"raw_fallback","status":"verified_fuzzy"},"standing_notice":{"events":[],"observation":"No event found in the named queried sources as of 2026-08-10T06:31:04.303077+00:00.","reason":null,"source_receipts":[{"observed_at":"2026-08-10T06:31:04.303077+00:00","source":"crossref"},{"observed_at":"2026-08-10T06:30:57.382061+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":"2026-08-09T14:24:43.536800Z","title":"Modeling parametric uncertainty in pdes models via physics-informed neural networks","venue":null,"work_id":"91a831db-7aa0-4ad0-a1d6-ff7428cdccbb","year":2025},"citing_paper":{"arxiv_id":"2502.01820","last_updated":"2025-02-03T20:54:37Z","snapshot_observed_at":"2026-08-10T11:53:01.884721Z","submitted_at":"2025-02-03T20:54:37Z","title":"Physics-Informed Surrogates for Temperature Prediction of Multi-Tracks in Laser Powder Bed Fusion","version":1},"reference_index":22,"source":"pdf_text","source_observed_at":"2026-08-09T14:24:43.364758Z"},"links":{"citing_paper":"/paper/2502.01820"},"observation_digest":"sha256:a57914b5eeb65bb62826456a3d0e13bbac61ff9c4f43db0daf413d6492ef654d","observation_id":"731d02ba-d021-4e06-b1aa-dacd9cc35ad4","resolution":{"observed_at":"2026-08-09T14:24:43.539703Z","resolver_source":"raw_fallback","status":"verified_fuzzy"},"standing_notice":{"events":[],"observation":"No event found in the named queried sources as of 2026-08-10T06:31:04.303077+00:00.","reason":null,"source_receipts":[{"observed_at":"2026-08-10T06:31:04.303077+00:00","source":"crossref"},{"observed_at":"2026-08-10T06:30:57.382061+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":"2026-08-09T14:24:43.367277Z","title":"Physics-informed neural networks: A deep learning framework for solving forward and inverse problems involving nonlinear partial differential equations","venue":null,"work_id":null,"year":2019},"citing_paper":{"arxiv_id":"2502.01820","last_updated":"2025-02-03T20:54:37Z","snapshot_observed_at":"2026-08-10T11:53:01.884721Z","submitted_at":"2025-02-03T20:54:37Z","title":"Physics-Informed Surrogates for Temperature Prediction of Multi-Tracks in Laser Powder Bed Fusion","version":1},"reference_index":23,"source":"pdf_text","source_observed_at":"2026-08-09T14:24:43.367277Z"},"links":{"citing_paper":"/paper/2502.01820"},"observation_digest":"sha256:d94ee7793fdb6b0fecab940753b46cf1d76138a9d404bcd33395e3f8575919a6","observation_id":"a06e7875-8a2e-4bc2-a4bd-565d92623ea1","resolution":{"observed_at":"2026-08-09T14:24:43.367277Z","resolver_source":null,"status":"unresolved"},"standing_notice":{"events":[],"reason":"canonical_work_link_unavailable","source_receipts":[],"state":"unavailable"}},{"citation":{"cited_paper":{"arxiv_id":"2402.01868","last_updated":"2024-06-03T23:35:42Z","snapshot_observed_at":"2026-08-10T11:52:09.961227Z","submitted_at":"2024-02-02T19:46:43Z","title":"Challenges in Training PINNs: A Loss Landscape Perspective","version":2},"cited_work":{"arxiv_id":null,"doi":null,"metadata_source":null,"pith_arxiv_id":"2402.01868","snapshot_observed_at":"2026-08-09T14:24:43.369780Z","title":"Challenges in training pinns: A loss landscape perspective","venue":null,"work_id":null,"year":2024},"citing_paper":{"arxiv_id":"2502.01820","last_updated":"2025-02-03T20:54:37Z","snapshot_observed_at":"2026-08-10T11:53:01.884721Z","submitted_at":"2025-02-03T20:54:37Z","title":"Physics-Informed Surrogates for Temperature Prediction of Multi-Tracks in Laser Powder Bed Fusion","version":1},"reference_index":24,"source":"pdf_text","source_observed_at":"2026-08-09T14:24:43.369780Z"},"links":{"cited_paper":"/paper/2402.01868","citing_paper":"/paper/2502.01820"},"observation_digest":"sha256:ad36ab828b879e275d438fcdd105ccfef8fd539fc1bd029458a5bf11cc79f886","observation_id":"ce4e9284-37e8-476a-a41e-8278ea4f42e8","resolution":{"observed_at":"2026-08-09T14:24:43.369780Z","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":"2026-08-09T14:24:43.525786Z","title":"Reduced and all-at-once approaches for model calibration and discovery in computational solid mechanics","venue":null,"work_id":"c5f84a09-a3a2-48b1-a2f8-61c144bea361","year":2024},"citing_paper":{"arxiv_id":"2502.01820","last_updated":"2025-02-03T20:54:37Z","snapshot_observed_at":"2026-08-10T11:53:01.884721Z","submitted_at":"2025-02-03T20:54:37Z","title":"Physics-Informed Surrogates for Temperature Prediction of Multi-Tracks in Laser Powder Bed Fusion","version":1},"reference_index":25,"source":"pdf_text","source_observed_at":"2026-08-09T14:24:43.372615Z"},"links":{"citing_paper":"/paper/2502.01820"},"observation_digest":"sha256:e9bb150f4750d17ba511211aae9bb193fb07f02d745fa4b10152b56420154d2c","observation_id":"3c0c7d89-2a06-46d9-a587-5986cc4e58c5","resolution":{"observed_at":"2026-08-09T14:24:43.528507Z","resolver_source":"raw_fallback","status":"verified_fuzzy"},"standing_notice":{"events":[],"observation":"No event found in the named queried sources as of 2026-08-10T06:31:04.303077+00:00.","reason":null,"source_receipts":[{"observed_at":"2026-08-10T06:31:04.303077+00:00","source":"crossref"},{"observed_at":"2026-08-10T06:30:57.382061+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":"2026-08-09T14:24:43.518649Z","title":"Advances in computational modeling for laser powder bed fusion additive manufacturing: A comprehensive review of finite element techniques and strategies","venue":null,"work_id":"064eabf5-6088-4355-aeb2-7a330d55f378","year":2024},"citing_paper":{"arxiv_id":"2502.01820","last_updated":"2025-02-03T20:54:37Z","snapshot_observed_at":"2026-08-10T11:53:01.884721Z","submitted_at":"2025-02-03T20:54:37Z","title":"Physics-Informed Surrogates for Temperature Prediction of Multi-Tracks in Laser Powder Bed Fusion","version":1},"reference_index":26,"source":"pdf_text","source_observed_at":"2026-08-09T14:24:43.375116Z"},"links":{"citing_paper":"/paper/2502.01820"},"observation_digest":"sha256:de7127682185fe14eaf3b853882126ca733613b0da2cb21bf40c909a364ba781","observation_id":"c5f5d1c0-125f-4efa-92b9-01885c1f7c2b","resolution":{"observed_at":"2026-08-09T14:24:43.521331Z","resolver_source":"raw_fallback","status":"verified_fuzzy"},"standing_notice":{"events":[],"observation":"No event found in the named queried sources as of 2026-08-10T06:31:04.303077+00:00.","reason":null,"source_receipts":[{"observed_at":"2026-08-10T06:31:04.303077+00:00","source":"crossref"},{"observed_at":"2026-08-10T06:30:57.382061+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":"2026-08-09T14:24:43.511297Z","title":"Simulation of metallic powder bed additive manufacturing processes with the finite element method: A critical review","venue":null,"work_id":"6089a5f6-cc90-456c-8cb9-088bc34e296e","year":2017},"citing_paper":{"arxiv_id":"2502.01820","last_updated":"2025-02-03T20:54:37Z","snapshot_observed_at":"2026-08-10T11:53:01.884721Z","submitted_at":"2025-02-03T20:54:37Z","title":"Physics-Informed Surrogates for Temperature Prediction of Multi-Tracks in Laser Powder Bed Fusion","version":1},"reference_index":27,"source":"pdf_text","source_observed_at":"2026-08-09T14:24:43.377652Z"},"links":{"citing_paper":"/paper/2502.01820"},"observation_digest":"sha256:c5900430a6d0e23c2748d593c29571c18d1e65528dc3517c2f111746364914f6","observation_id":"fbc6c769-43b2-4f27-97cf-1f27528cbb72","resolution":{"observed_at":"2026-08-09T14:24:43.514054Z","resolver_source":"raw_fallback","status":"verified_fuzzy"},"standing_notice":{"events":[],"observation":"No event found in the named queried sources as of 2026-08-10T06:31:04.303077+00:00.","reason":null,"source_receipts":[{"observed_at":"2026-08-10T06:31:04.303077+00:00","source":"crossref"},{"observed_at":"2026-08-10T06:30:57.382061+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":"2026-08-09T14:24:43.503839Z","title":"On the distribution of points in a cube and the approximate evaluation of integrals","venue":null,"work_id":"deb7428a-0f63-42a1-abb0-5a5b4e5e48a6","year":1967},"citing_paper":{"arxiv_id":"2502.01820","last_updated":"2025-02-03T20:54:37Z","snapshot_observed_at":"2026-08-10T11:53:01.884721Z","submitted_at":"2025-02-03T20:54:37Z","title":"Physics-Informed Surrogates for Temperature Prediction of Multi-Tracks in Laser Powder Bed Fusion","version":1},"reference_index":28,"source":"pdf_text","source_observed_at":"2026-08-09T14:24:43.380199Z"},"links":{"citing_paper":"/paper/2502.01820"},"observation_digest":"sha256:db9746cff256e12beee339597db2ffc1575c186519cf0367bb43205725932805","observation_id":"f51836d5-7a4f-47e5-b645-5315d95ec3f4","resolution":{"observed_at":"2026-08-09T14:24:43.506652Z","resolver_source":"raw_fallback","status":"verified_fuzzy"},"standing_notice":{"events":[],"observation":"No event found in the named queried sources as of 2026-08-10T06:31:04.303077+00:00.","reason":null,"source_receipts":[{"observed_at":"2026-08-10T06:31:04.303077+00:00","source":"crossref"},{"observed_at":"2026-08-10T06:30:57.382061+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":"2026-08-09T14:24:43.496398Z","title":"Pgd in thermal transient problems with a moving heat source: A sensitivity study on factors affecting accuracy and efficiency","venue":null,"work_id":"87c81850-44c5-48b3-a8dd-275a97e16add","year":2024},"citing_paper":{"arxiv_id":"2502.01820","last_updated":"2025-02-03T20:54:37Z","snapshot_observed_at":"2026-08-10T11:53:01.884721Z","submitted_at":"2025-02-03T20:54:37Z","title":"Physics-Informed Surrogates for Temperature Prediction of Multi-Tracks in Laser Powder Bed Fusion","version":1},"reference_index":29,"source":"pdf_text","source_observed_at":"2026-08-09T14:24:43.382578Z"},"links":{"citing_paper":"/paper/2502.01820"},"observation_digest":"sha256:fe6da9e70b3c4630730c5405b5d0679e644c1df76c5e660da3b971dae849798e","observation_id":"68d587ca-613b-4d30-bfc0-437c5c48c63e","resolution":{"observed_at":"2026-08-09T14:24:43.499237Z","resolver_source":"raw_fallback","status":"verified_fuzzy"},"standing_notice":{"events":[],"observation":"No event found in the named queried sources as of 2026-08-10T06:31:04.303077+00:00.","reason":null,"source_receipts":[{"observed_at":"2026-08-10T06:31:04.303077+00:00","source":"crossref"},{"observed_at":"2026-08-10T06:30:57.382061+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":"2026-08-09T14:24:43.488893Z","title":"On the calibration of thermo-microstructural simulation models for laser powder bed fusion process: Integrating physics-informed neural networks with cellular automata","venue":null,"work_id":"d6b8c229-8dbe-4a2a-a7ac-1c7a535b2591","year":2024},"citing_paper":{"arxiv_id":"2502.01820","last_updated":"2025-02-03T20:54:37Z","snapshot_observed_at":"2026-08-10T11:53:01.884721Z","submitted_at":"2025-02-03T20:54:37Z","title":"Physics-Informed Surrogates for Temperature Prediction of Multi-Tracks in Laser Powder Bed Fusion","version":1},"reference_index":30,"source":"pdf_text","source_observed_at":"2026-08-09T14:24:43.385198Z"},"links":{"citing_paper":"/paper/2502.01820"},"observation_digest":"sha256:c813559fc359025701961a76e22888ce99eb6aa8986a994821e7ecba36f782f3","observation_id":"0c0c1275-205a-4b8b-ba51-fc9ffe627d91","resolution":{"observed_at":"2026-08-09T14:24:43.491726Z","resolver_source":"raw_fallback","status":"verified_fuzzy"},"standing_notice":{"events":[],"observation":"No event found in the named queried sources as of 2026-08-10T06:31:04.303077+00:00.","reason":null,"source_receipts":[{"observed_at":"2026-08-10T06:31:04.303077+00:00","source":"crossref"},{"observed_at":"2026-08-10T06:30:57.382061+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":"2026-08-09T14:24:43.481607Z","title":"Wavelet neural operator for solving parametric partial dif- ferential equations in computational mechanics problems","venue":null,"work_id":"23ff0204-ccb2-4098-88c1-c4c4b1dcf916","year":2023},"citing_paper":{"arxiv_id":"2502.01820","last_updated":"2025-02-03T20:54:37Z","snapshot_observed_at":"2026-08-10T11:53:01.884721Z","submitted_at":"2025-02-03T20:54:37Z","title":"Physics-Informed Surrogates for Temperature Prediction of Multi-Tracks in Laser Powder Bed Fusion","version":1},"reference_index":31,"source":"pdf_text","source_observed_at":"2026-08-09T14:24:43.387563Z"},"links":{"citing_paper":"/paper/2502.01820"},"observation_digest":"sha256:475245f90f38c066624bb6a2b8a40d783fcd3950196a72522ef1a968066bec82","observation_id":"25d885c9-8214-4f82-a0a9-e4667c27f7c7","resolution":{"observed_at":"2026-08-09T14:24:43.484429Z","resolver_source":"raw_fallback","status":"verified_fuzzy"},"standing_notice":{"events":[],"observation":"No event found in the named queried sources as of 2026-08-10T06:31:04.303077+00:00.","reason":null,"source_receipts":[{"observed_at":"2026-08-10T06:31:04.303077+00:00","source":"crossref"},{"observed_at":"2026-08-10T06:30:57.382061+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":"2026-08-09T14:24:43.473662Z","title":"3d temperature field prediction in direct energy deposition of metals using physics informed neural network","venue":null,"work_id":"4f9379f2-6c53-47b0-94d9-891b37566902","year":2022},"citing_paper":{"arxiv_id":"2502.01820","last_updated":"2025-02-03T20:54:37Z","snapshot_observed_at":"2026-08-10T11:53:01.884721Z","submitted_at":"2025-02-03T20:54:37Z","title":"Physics-Informed Surrogates for Temperature Prediction of Multi-Tracks in Laser Powder Bed Fusion","version":1},"reference_index":32,"source":"pdf_text","source_observed_at":"2026-08-09T14:24:43.390132Z"},"links":{"citing_paper":"/paper/2502.01820"},"observation_digest":"sha256:337d79f5f9d341e1791b0f55876a5659feb886f2155362ce7b37995f453a40ea","observation_id":"a5b760eb-b56a-4869-929d-cf5f3a66423d","resolution":{"observed_at":"2026-08-09T14:24:43.476615Z","resolver_source":"raw_fallback","status":"verified_fuzzy"},"standing_notice":{"events":[],"observation":"No event found in the named queried sources as of 2026-08-10T06:31:04.303077+00:00.","reason":null,"source_receipts":[{"observed_at":"2026-08-10T06:31:04.303077+00:00","source":"crossref"},{"observed_at":"2026-08-10T06:30:57.382061+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":"2026-08-09T14:24:43.466230Z","title":"Process planning for adaptive contour parallel tool- path in additive manufacturing with variable bead width","venue":null,"work_id":"e5f4a74d-b09c-41ad-bf6b-95f909d00499","year":2019},"citing_paper":{"arxiv_id":"2502.01820","last_updated":"2025-02-03T20:54:37Z","snapshot_observed_at":"2026-08-10T11:53:01.884721Z","submitted_at":"2025-02-03T20:54:37Z","title":"Physics-Informed Surrogates for Temperature Prediction of Multi-Tracks in Laser Powder Bed Fusion","version":1},"reference_index":33,"source":"pdf_text","source_observed_at":"2026-08-09T14:24:43.392492Z"},"links":{"citing_paper":"/paper/2502.01820"},"observation_digest":"sha256:36e496e8f743643905b8e890e45e8b406b7a20c2045bb5f1bd0e221b27b1f08f","observation_id":"a75af393-a3c1-45a3-9930-7fd12c362f9b","resolution":{"observed_at":"2026-08-09T14:24:43.469025Z","resolver_source":"raw_fallback","status":"verified_fuzzy"},"standing_notice":{"events":[],"observation":"No event found in the named queried sources as of 2026-08-10T06:31:04.303077+00:00.","reason":null,"source_receipts":[{"observed_at":"2026-08-10T06:31:04.303077+00:00","source":"crossref"},{"observed_at":"2026-08-10T06:30:57.382061+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":"2026-08-09T14:24:43.458514Z","title":"Fast and accurate reduced-order modeling of a moose-based additive manufacturing model with operator learning","venue":null,"work_id":"81743fd6-4cea-4366-aff3-591633e5a269","year":2023},"citing_paper":{"arxiv_id":"2502.01820","last_updated":"2025-02-03T20:54:37Z","snapshot_observed_at":"2026-08-10T11:53:01.884721Z","submitted_at":"2025-02-03T20:54:37Z","title":"Physics-Informed Surrogates for Temperature Prediction of Multi-Tracks in Laser Powder Bed Fusion","version":1},"reference_index":34,"source":"pdf_text","source_observed_at":"2026-08-09T14:24:43.394862Z"},"links":{"citing_paper":"/paper/2502.01820"},"observation_digest":"sha256:39382188eaee6051b23412786ecc1a18ac671ff937d7a6a85fd3ed33d4a2b2b7","observation_id":"4fc8827d-d6cc-482d-8056-e40784d7408b","resolution":{"observed_at":"2026-08-09T14:24:43.461281Z","resolver_source":"raw_fallback","status":"verified_fuzzy"},"standing_notice":{"events":[],"observation":"No event found in the named queried sources as of 2026-08-10T06:31:04.303077+00:00.","reason":null,"source_receipts":[{"observed_at":"2026-08-10T06:31:04.303077+00:00","source":"crossref"},{"observed_at":"2026-08-10T06:30:57.382061+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":"2026-08-09T14:24:43.450602Z","title":"Modeling and cooling rate control in laser additive manufacturing: 1-d pde formulation","venue":null,"work_id":"3889fc01-e1d0-4888-9967-839796fedad2","year":2017},"citing_paper":{"arxiv_id":"2502.01820","last_updated":"2025-02-03T20:54:37Z","snapshot_observed_at":"2026-08-10T11:53:01.884721Z","submitted_at":"2025-02-03T20:54:37Z","title":"Physics-Informed Surrogates for Temperature Prediction of Multi-Tracks in Laser Powder Bed Fusion","version":1},"reference_index":35,"source":"pdf_text","source_observed_at":"2026-08-09T14:24:43.397191Z"},"links":{"citing_paper":"/paper/2502.01820"},"observation_digest":"sha256:263cb36af7e49ec335b869a16018377371b276f0a584a4a01c7f6a76fa2ed608","observation_id":"c1d0e7f7-ee44-499c-9a2a-038b6b9a08e8","resolution":{"observed_at":"2026-08-09T14:24:43.453448Z","resolver_source":"raw_fallback","status":"verified_fuzzy"},"standing_notice":{"events":[],"observation":"No event found in the named queried sources as of 2026-08-10T06:31:04.303077+00:00.","reason":null,"source_receipts":[{"observed_at":"2026-08-10T06:31:04.303077+00:00","source":"crossref"},{"observed_at":"2026-08-10T06:30:57.382061+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":"2026-08-09T14:24:43.443042Z","title":null,"venue":null,"work_id":"2e769720-1c4f-4102-80ca-34bd4a66ceb7","year":2021},"citing_paper":{"arxiv_id":"2502.01820","last_updated":"2025-02-03T20:54:37Z","snapshot_observed_at":"2026-08-10T11:53:01.884721Z","submitted_at":"2025-02-03T20:54:37Z","title":"Physics-Informed Surrogates for Temperature Prediction of Multi-Tracks in Laser Powder Bed Fusion","version":1},"reference_index":36,"source":"pdf_text","source_observed_at":"2026-08-09T14:24:43.399654Z"},"links":{"citing_paper":"/paper/2502.01820"},"observation_digest":"sha256:b9464eeda1c5dc1d376bb6479ec909384fcd1b04d854fc1b506578d57354bd8b","observation_id":"c591a04f-8a69-451b-9d02-2a084aeba963","resolution":{"observed_at":"2026-08-09T14:24:43.445719Z","resolver_source":"raw_fallback","status":"unresolved"},"standing_notice":{"events":[],"observation":"No event found in the named queried sources as of 2026-08-10T06:31:04.303077+00:00.","reason":null,"source_receipts":[{"observed_at":"2026-08-10T06:31:04.303077+00:00","source":"crossref"},{"observed_at":"2026-08-10T06:30:57.382061+00:00","source":"retraction_watch"}],"state":"measured"}}],"paper":{"arxiv_id":"2502.01820","last_updated":"2025-02-03T20:54:37Z","latest_version":1,"primary_category":"cs.CE","snapshot_observed_at":"2026-08-10T11:53:01.884721Z","submitted_at":"2025-02-03T20:54:37Z","title":"Physics-Informed Surrogates for Temperature Prediction of Multi-Tracks in Laser Powder Bed Fusion"},"reference_resolution":{"displayed":36,"state_counts":{"malformed_identifier":0,"metadata_mismatch":0,"parse_uncertain":0,"unresolved":7,"verified_exact":1,"verified_fuzzy":28},"total_outbound_references":36},"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":"2026-08-10T06:31:04.303077+00:00","source":"crossref"},{"observed_at":"2026-08-10T06:30:57.382061+00:00","source":"retraction_watch"}],"thesis":"As of 10 August 2026, this Paper Citation Record lists 36 of 36 outbound references and 1 inbound Pith citation observation for arXiv:2502.01820."}