{"id":"535b19ed1c1399b5eb021c3137b61520371c9a2f7aa003b6fdb2d36bac466efb","title":"DyPSI: Dynamic Physics Sensing via Joint Field and Sensor-Trajectory Generation","argument":"The paper’s authors introduce DyPSI, a framework that reconstructs changing physical fields from sparse observations while deciding where sensors should collect future observations. They argue that methods using one sensor layout across all frames, or choosing placements independently frame by frame, can limit temporally coherent sensing trajectories.\n\nDyPSI generates future fields and sensor trajectories together, conditioned on historical observations. It represents sensor coordinates as Sensor Position Fields, encodes fields and sensing configurations with a shared Functional Tucker representation, and trains a joint diffusion model. A Gaussian-process temporal kernel correlates perturbations during training, biasing the denoiser toward temporally smooth recoveries. The authors also link the EDM training objective to an A-optimality criterion and provide a bound on discrete trajectory variation.\n\nIn experiments on turbulent flow, GLORYS12 sea-surface temperature, and 3D car aerodynamics, the authors report that DyPSI outperforms static and frame-wise placement strategies, with substantial error reduction under tight sensor budgets. The abstract does not provide numerical effect sizes, and the reported experiments do not establish performance in other settings.","topicIds":["intelligence-and-discovery"],"domain":"science","claimType":"attributed reading","limits":"The supplied evidence presents the paper’s abstract, not detailed experimental results; it gives no numerical effect sizes for the reported error reductions. The abstract reports results for turbulent flow, GLORYS12 sea-surface temperature, and 3D car aerodynamics; it does not establish performance in other settings.","speaker":{"name":"Zhang, Yizhou, Chen, Panqi, Cheng, Lei, Zhang, Ting, Li, Jianlong, Shi, Jianfeng, Fang, Shikai","roleAtSource":"Article author"},"source":{"title":"DyPSI: Dynamic Physics Sensing via Joint Field and Sensor-Trajectory Generation","publisher":"arXiv cs.AI","format":"Article","url":"https://arxiv.org/abs/2611.06280","publishedAt":"2026-11-04","dateBasis":"Date basis not provided."},"evidence":[{"text":"The authors present DyPSI as jointly generating future fields and sensor trajectories from historical observations.","excerpt":"formulates this problem as the joint generation of future fields and sensor trajectories conditioned on historical observations","url":"https://arxiv.org/abs/2611.06280","relationship":"Source-stated"},{"text":"DyPSI combines Sensor Position Fields, a shared Functional Tucker representation, and a joint diffusion model.","excerpt":"encodes fields and sensing configurations with a shared Functional Tucker representation, and trains a joint diffusion model","url":"https://arxiv.org/abs/2611.06280","relationship":"Source-stated"},{"text":"The authors report that DyPSI outperforms static and frame-wise placement strategies in three stated experiment domains, with substantial error reduction under tight sensor budgets.","excerpt":"Experiments on turbulent flow, GLORYS12 sea-surface temperature, and 3D car aerodynamics show that DyPSI consistently outperforms static and frame-wise placement strategies, with substantial error reduction under tight sensor budgets.","url":"https://arxiv.org/abs/2611.06280","relationship":"Source-stated"}],"sourceMedia":{"status":"not_found","sourceUrl":"https://arxiv.org/abs/2611.06280","checkedAt":"2026-11-09T10:38:10.238Z","items":[],"note":"No suitable media was found by the supported page extraction."},"assumptions":[],"openQuestion":{"attribution":"Open question","text":"How large were the reported error reductions under tight sensor budgets in each of the three experiments?"},"whatWouldChangeView":{"attribution":"Change condition not recorded","text":""},"editorialComparisons":[],"live":{"title":"DyPSI: Dynamic Physics Sensing via Joint Field and Sensor-Trajectory Generation","summary":"The paper’s authors introduce DyPSI, a framework that reconstructs changing physical fields from sparse observations while deciding where sensors should collect future observations. They argue that methods using one sensor layout across all frames, or choosing placements independently frame by frame, can limit temporally coherent sensing trajectories.\n\nDyPSI generates future fields and sensor trajectories together, conditioned on historical observations. It represents sensor coordinates as Sensor Position Fields, encodes fields and sensing configurations with a shared Functional Tucker representation, and trains a joint diffusion model. A Gaussian-process temporal kernel correlates perturbations during training, biasing the denoiser toward temporally smooth recoveries. The authors also link the EDM training objective to an A-optimality criterion and provide a bound on discrete trajectory variation.\n\nIn experiments on turbulent flow, GLORYS12 sea-surface temperature, and 3D car aerodynamics, the authors report that DyPSI outperforms static and frame-wise placement strategies, with substantial error reduction under tight sensor budgets. The abstract does not provide numerical effect sizes, and the reported experiments do not establish performance in other settings.","question":"How large were the reported error reductions under tight sensor budgets in each of the three experiments?","claims":[{"text":"The authors present DyPSI as jointly generating future fields and sensor trajectories from historical observations.","excerpt":"formulates this problem as the joint generation of future fields and sensor trajectories conditioned on historical observations","support":"source-stated"},{"text":"DyPSI combines Sensor Position Fields, a shared Functional Tucker representation, and a joint diffusion model.","excerpt":"encodes fields and sensing configurations with a shared Functional Tucker representation, and trains a joint diffusion model","support":"source-stated"},{"text":"The authors report that DyPSI outperforms static and frame-wise placement strategies in three stated experiment domains, with substantial error reduction under tight sensor budgets.","excerpt":"Experiments on turbulent flow, GLORYS12 sea-surface temperature, and 3D car aerodynamics show that DyPSI consistently outperforms static and frame-wise placement strategies, with substantial error reduction under tight sensor budgets.","support":"source-stated"}],"limitations":["The supplied evidence presents the paper’s abstract, not detailed experimental results; it gives no numerical effect sizes for the reported error reductions.","The abstract reports results for turbulent flow, GLORYS12 sea-surface temperature, and 3D car aerodynamics; it does not establish performance in other settings."],"mediaId":"28929f1b89f93f522b0964260be4a07e5eee820477109b884ea07f1df87ac448","correctionReason":null,"id":"535b19ed1c1399b5eb021c3137b61520371c9a2f7aa003b6fdb2d36bac466efb","publicationId":"eeba1566-eb29-48dc-8d7e-ab04ca8ba691","version":1,"sourceUrl":"https://arxiv.org/abs/2611.06280","sourceTitle":"DyPSI: Dynamic Physics Sensing via Joint Field and Sensor-Trajectory Generation","publisher":"arXiv cs.AI","publishedAt":null,"originalPublished":{"value":"2026-11-04","precision":"day","basis":"publisher_citation_date"},"firstObservedAt":"2026-11-05T05:00:36.713Z","createdAt":"2026-11-09T09:34:35.122Z","historical":true,"entityIds":["arxiv"],"topicIds":["intelligence-and-discovery"],"media":[{"id":"28929f1b89f93f522b0964260be4a07e5eee820477109b884ea07f1df87ac448","kind":"article","url":"https://arxiv.org/abs/2611.06280","sourceUrl":"https://arxiv.org/abs/2611.06280","label":"Read original","provenance":"source_link","verification":{"status":"verified","checkedAt":"2026-11-09T09:33:42.195Z","method":"acquired_source","contentType":"text/html"},"recommendationReason":"Recommended by Jev for understanding this source; checked by Luna."}],"sourceMedia":{"status":"not_found","sourceUrl":"https://arxiv.org/abs/2611.06280","checkedAt":"2026-11-09T10:38:10.238Z","items":[],"note":"No suitable media was found by the supported page extraction."},"editorialDecision":{"outcome":"accept","reasonCodes":["material_development"],"rubricVersion":"stream-admission-v5","mediaId":"28929f1b89f93f522b0964260be4a07e5eee820477109b884ea07f1df87ac448","confidence":0.78,"provider":"typesafe","model":"jev-1.13.0","inputHash":"29ee1acb6aa0240c1be79cdc128000647ed118a9292966bd95943b2e4e0eee9e","decidedAt":"2026-11-09T09:33:44.153Z"},"provenance":{"textHash":"5a0e73af9ae6e0117b9f76842223180915de4d58f4573a8d58dc516835c757c5","observationId":"42812e52b0be4e9042a6d2ba61d5ffdc52d1573d1c02fba99115c18e8e796dbf","retrievedAt":"2026-11-09T09:33:41.858Z","kind":"publisher-page","preparer":{"model":"gpt-6-luna","runId":"8277932f-f035-4f9e-831f-75fcf6ced081"},"reviewer":{"model":"gpt-6-luna","runId":"7910a7e1-113f-4aa1-8ec1-5793e4c312f3"}},"audit":{"status":"passed","reviewedAt":"2026-11-09T09:34:35.122Z","model":"anthropic/claude-opus-5.5"},"sourceAttribution":{"authors":[{"name":"Zhang, Yizhou","url":null,"kind":"unknown"},{"name":"Chen, Panqi","url":null,"kind":"unknown"},{"name":"Cheng, Lei","url":null,"kind":"unknown"},{"name":"Zhang, Ting","url":null,"kind":"unknown"},{"name":"Li, Jianlong","url":null,"kind":"unknown"},{"name":"Shi, Jianfeng","url":null,"kind":"unknown"},{"name":"Fang, Shikai","url":null,"kind":"unknown"}],"authorRole":"article-author","hosts":[],"status":"stated","authorRoster":{"truncated":false,"omitted":0,"assertionGroupsOmitted":0},"participation":[{"actor":{"name":"Zhang, Yizhou","url":null,"kind":"unknown","actorId":null},"role":"author","scope":{"kind":"work-credit","locator":null},"literalCredit":"Zhang, Yizhou","status":"stated","provenance":{"basis":"head-meta","sourceUrl":"https://arxiv.org/abs/2611.06280","locator":"head/meta[name=author|citation_author]"},"assertionId":"62184e9ebc0689465b64fafd9638d0f28f79f94a3d991bce2e78743cd8219253:participation:0","identity":{"version":"entity-identity-v1","status":"unresolved","actorId":null,"canonicalActorId":null,"candidateActorIds":[],"assertions":[],"sourceObservedAt":"2026-11-09T10:38:10.238Z","knowledgeAsOf":null},"sourceVersion":{"snapshotId":"62184e9ebc0689465b64fafd9638d0f28f79f94a3d991bce2e78743cd8219253","sourceUrl":"https://arxiv.org/abs/2611.06280","contentSha256":"3c02ee5687891710c09fe6c351069675d6a3a0bb61467479f4a38d2ed93c2545","textHash":"5a0e73af9ae6e0117b9f76842223180915de4d58f4573a8d58dc516835c757c5","observedAt":"2026-11-09T10:38:10.238Z"}},{"actor":{"name":"Chen, Panqi","url":null,"kind":"unknown","actorId":null},"role":"author","scope":{"kind":"work-credit","locator":null},"literalCredit":"Chen, Panqi","status":"stated","provenance":{"basis":"head-meta","sourceUrl":"https://arxiv.org/abs/2611.06280","locator":"head/meta[name=author|citation_author]"},"assertionId":"62184e9ebc0689465b64fafd9638d0f28f79f94a3d991bce2e78743cd8219253:participation:1","identity":{"version":"entity-identity-v1","status":"unresolved","actorId":null,"canonicalActorId":null,"candidateActorIds":[],"assertions":[],"sourceObservedAt":"2026-11-09T10:38:10.238Z","knowledgeAsOf":null},"sourceVersion":{"snapshotId":"62184e9ebc0689465b64fafd9638d0f28f79f94a3d991bce2e78743cd8219253","sourceUrl":"https://arxiv.org/abs/2611.06280","contentSha256":"3c02ee5687891710c09fe6c351069675d6a3a0bb61467479f4a38d2ed93c2545","textHash":"5a0e73af9ae6e0117b9f76842223180915de4d58f4573a8d58dc516835c757c5","observedAt":"2026-11-09T10:38:10.238Z"}},{"actor":{"name":"Cheng, Lei","url":null,"kind":"unknown","actorId":null},"role":"author","scope":{"kind":"work-credit","locator":null},"literalCredit":"Cheng, Lei","status":"stated","provenance":{"basis":"head-meta","sourceUrl":"https://arxiv.org/abs/2611.06280","locator":"head/meta[name=author|citation_author]"},"assertionId":"62184e9ebc0689465b64fafd9638d0f28f79f94a3d991bce2e78743cd8219253:participation:2","identity":{"version":"entity-identity-v1","status":"unresolved","actorId":null,"canonicalActorId":null,"candidateActorIds":[],"assertions":[],"sourceObservedAt":"2026-11-09T10:38:10.238Z","knowledgeAsOf":null},"sourceVersion":{"snapshotId":"62184e9ebc0689465b64fafd9638d0f28f79f94a3d991bce2e78743cd8219253","sourceUrl":"https://arxiv.org/abs/2611.06280","contentSha256":"3c02ee5687891710c09fe6c351069675d6a3a0bb61467479f4a38d2ed93c2545","textHash":"5a0e73af9ae6e0117b9f76842223180915de4d58f4573a8d58dc516835c757c5","observedAt":"2026-11-09T10:38:10.238Z"}},{"actor":{"name":"Zhang, Ting","url":null,"kind":"unknown","actorId":null},"role":"author","scope":{"kind":"work-credit","locator":null},"literalCredit":"Zhang, Ting","status":"stated","provenance":{"basis":"head-meta","sourceUrl":"https://arxiv.org/abs/2611.06280","locator":"head/meta[name=author|citation_author]"},"assertionId":"62184e9ebc0689465b64fafd9638d0f28f79f94a3d991bce2e78743cd8219253:participation:3","identity":{"version":"entity-identity-v1","status":"unresolved","actorId":null,"canonicalActorId":null,"candidateActorIds":[],"assertions":[],"sourceObservedAt":"2026-11-09T10:38:10.238Z","knowledgeAsOf":null},"sourceVersion":{"snapshotId":"62184e9ebc0689465b64fafd9638d0f28f79f94a3d991bce2e78743cd8219253","sourceUrl":"https://arxiv.org/abs/2611.06280","contentSha256":"3c02ee5687891710c09fe6c351069675d6a3a0bb61467479f4a38d2ed93c2545","textHash":"5a0e73af9ae6e0117b9f76842223180915de4d58f4573a8d58dc516835c757c5","observedAt":"2026-11-09T10:38:10.238Z"}},{"actor":{"name":"Li, Jianlong","url":null,"kind":"unknown","actorId":null},"role":"author","scope":{"kind":"work-credit","locator":null},"literalCredit":"Li, Jianlong","status":"stated","provenance":{"basis":"head-meta","sourceUrl":"https://arxiv.org/abs/2611.06280","locator":"head/meta[name=author|citation_author]"},"assertionId":"62184e9ebc0689465b64fafd9638d0f28f79f94a3d991bce2e78743cd8219253:participation:4","identity":{"version":"entity-identity-v1","status":"unresolved","actorId":null,"canonicalActorId":null,"candidateActorIds":[],"assertions":[],"sourceObservedAt":"2026-11-09T10:38:10.238Z","knowledgeAsOf":null},"sourceVersion":{"snapshotId":"62184e9ebc0689465b64fafd9638d0f28f79f94a3d991bce2e78743cd8219253","sourceUrl":"https://arxiv.org/abs/2611.06280","contentSha256":"3c02ee5687891710c09fe6c351069675d6a3a0bb61467479f4a38d2ed93c2545","textHash":"5a0e73af9ae6e0117b9f76842223180915de4d58f4573a8d58dc516835c757c5","observedAt":"2026-11-09T10:38:10.238Z"}},{"actor":{"name":"Shi, Jianfeng","url":null,"kind":"unknown","actorId":null},"role":"author","scope":{"kind":"work-credit","locator":null},"literalCredit":"Shi, Jianfeng","status":"stated","provenance":{"basis":"head-meta","sourceUrl":"https://arxiv.org/abs/2611.06280","locator":"head/meta[name=author|citation_author]"},"assertionId":"62184e9ebc0689465b64fafd9638d0f28f79f94a3d991bce2e78743cd8219253:participation:5","identity":{"version":"entity-identity-v1","status":"unresolved","actorId":null,"canonicalActorId":null,"candidateActorIds":[],"assertions":[],"sourceObservedAt":"2026-11-09T10:38:10.238Z","knowledgeAsOf":null},"sourceVersion":{"snapshotId":"62184e9ebc0689465b64fafd9638d0f28f79f94a3d991bce2e78743cd8219253","sourceUrl":"https://arxiv.org/abs/2611.06280","contentSha256":"3c02ee5687891710c09fe6c351069675d6a3a0bb61467479f4a38d2ed93c2545","textHash":"5a0e73af9ae6e0117b9f76842223180915de4d58f4573a8d58dc516835c757c5","observedAt":"2026-11-09T10:38:10.238Z"}},{"actor":{"name":"Fang, Shikai","url":null,"kind":"unknown","actorId":null},"role":"author","scope":{"kind":"work-credit","locator":null},"literalCredit":"Fang, Shikai","status":"stated","provenance":{"basis":"head-meta","sourceUrl":"https://arxiv.org/abs/2611.06280","locator":"head/meta[name=author|citation_author]"},"assertionId":"62184e9ebc0689465b64fafd9638d0f28f79f94a3d991bce2e78743cd8219253:participation:6","identity":{"version":"entity-identity-v1","status":"unresolved","actorId":null,"canonicalActorId":null,"candidateActorIds":[],"assertions":[],"sourceObservedAt":"2026-11-09T10:38:10.238Z","knowledgeAsOf":null},"sourceVersion":{"snapshotId":"62184e9ebc0689465b64fafd9638d0f28f79f94a3d991bce2e78743cd8219253","sourceUrl":"https://arxiv.org/abs/2611.06280","contentSha256":"3c02ee5687891710c09fe6c351069675d6a3a0bb61467479f4a38d2ed93c2545","textHash":"5a0e73af9ae6e0117b9f76842223180915de4d58f4573a8d58dc516835c757c5","observedAt":"2026-11-09T10:38:10.238Z"}}],"participationCoverage":{"total":7,"returned":7,"complete":true,"fullAssertions":"source-metadata-endpoint"},"distribution":null,"publisher":null,"sourceUrl":"https://arxiv.org/abs/2611.06280","snapshotId":"62184e9ebc0689465b64fafd9638d0f28f79f94a3d991bce2e78743cd8219253","contentSha256":"3c02ee5687891710c09fe6c351069675d6a3a0bb61467479f4a38d2ed93c2545","textHash":"5a0e73af9ae6e0117b9f76842223180915de4d58f4573a8d58dc516835c757c5","observedAt":"2026-11-09T10:38:10.238Z","basis":"publisher-page-metadata","sourceLinks":46,"linksTruncated":false,"metadataUrl":"/api/pipeline/stream/535b19ed1c1399b5eb021c3137b61520371c9a2f7aa003b6fdb2d36bac466efb/source"}},"canonicalUrl":"https://escapemeridian.com/readings/dypsi-dynamic-physics-sensing-via-joint-field-and-sensor-535b19ed","formats":{"html":"/readings/dypsi-dynamic-physics-sensing-via-joint-field-and-sensor-535b19ed","markdown":"/readings/dypsi-dynamic-physics-sensing-via-joint-field-and-sensor-535b19ed.md","json":"/readings/dypsi-dynamic-physics-sensing-via-joint-field-and-sensor-535b19ed.json"},"attribution":"Headline and summary by Escape Meridian, prepared by Luna from the attributed source. A source check is not independent confirmation of the source’s claims.","maintainedGuideLinks":[],"readerContext":{"version":"reader-context-v1","attribution":"Reference context selected by Escape Meridian; separate from source evidence. Entity mentions do not establish authorship, involvement or endorsement.","terms":[{"id":"term-diffusion-model","name":"Diffusion model","explanation":"A kind of generative AI that makes images, audio or video by starting from random noise and refining it step by step.","referenceUrl":"https://en.wikipedia.org/wiki/Diffusion_model"}],"entities":[{"id":"arxiv","name":"arXiv","kind":"organization","context":"arXiv is a research manuscript repository where authors share papers that may not yet have been peer reviewed.","referenceUrls":["https://arxiv.org/","https://info.arxiv.org/help/api/index.html"],"path":"/entities/arxiv","json":"/entities/arxiv.json","markdown":"/entities/arxiv.md"}]},"researchQuestions":[],"withdrawn":false,"references":{"schemaVersion":1,"target":{"type":"reading","id":"535b19ed1c1399b5eb021c3137b61520371c9a2f7aa003b6fdb2d36bac466efb","revision":"eeba1566-eb29-48dc-8d7e-ab04ca8ba691","revisionKind":"publication-id","title":"DyPSI: Dynamic Physics Sensing via Joint Field and Sensor-Trajectory Generation","url":"https://escapemeridian.com/readings/dypsi-dynamic-physics-sensing-via-joint-field-and-sensor-535b19ed"},"source":{"type":"source","id":"https://arxiv.org/abs/2611.06280","revision":"5a0e73af9ae6e0117b9f76842223180915de4d58f4573a8d58dc516835c757c5","revisionKind":"sha256-source-text","title":"DyPSI: Dynamic Physics Sensing via Joint Field and Sensor-Trajectory Generation","url":"https://arxiv.org/abs/2611.06280"},"status":"available","items":[],"next":null,"scope":"Explicit citations in public readings and public file attachments to this reading. Shared topics are not citations. Other artifact types need an explicit reference adapter."},"revisions":[{"id":"535b19ed1c1399b5eb021c3137b61520371c9a2f7aa003b6fdb2d36bac466efb","title":"DyPSI: Dynamic Physics Sensing via Joint Field and Sensor-Trajectory Generation","argument":"The paper’s authors introduce DyPSI, a framework that reconstructs changing physical fields from sparse observations while deciding where sensors should collect future observations. They argue that methods using one sensor layout across all frames, or choosing placements independently frame by frame, can limit temporally coherent sensing trajectories.\n\nDyPSI generates future fields and sensor trajectories together, conditioned on historical observations. It represents sensor coordinates as Sensor Position Fields, encodes fields and sensing configurations with a shared Functional Tucker representation, and trains a joint diffusion model. A Gaussian-process temporal kernel correlates perturbations during training, biasing the denoiser toward temporally smooth recoveries. The authors also link the EDM training objective to an A-optimality criterion and provide a bound on discrete trajectory variation.\n\nIn experiments on turbulent flow, GLORYS12 sea-surface temperature, and 3D car aerodynamics, the authors report that DyPSI outperforms static and frame-wise placement strategies, with substantial error reduction under tight sensor budgets. The abstract does not provide numerical effect sizes, and the reported experiments do not establish performance in other settings.","topicIds":["intelligence-and-discovery"],"domain":"science","claimType":"attributed reading","limits":"The supplied evidence presents the paper’s abstract, not detailed experimental results; it gives no numerical effect sizes for the reported error reductions. The abstract reports results for turbulent flow, GLORYS12 sea-surface temperature, and 3D car aerodynamics; it does not establish performance in other settings.","speaker":{"name":"Zhang, Yizhou, Chen, Panqi, Cheng, Lei, Zhang, Ting, Li, Jianlong, Shi, Jianfeng, Fang, Shikai","roleAtSource":"Article author"},"source":{"title":"DyPSI: Dynamic Physics Sensing via Joint Field and Sensor-Trajectory Generation","publisher":"arXiv cs.AI","format":"Article","url":"https://arxiv.org/abs/2611.06280","publishedAt":"2026-11-04","dateBasis":"Date basis not provided."},"evidence":[{"text":"The authors present DyPSI as jointly generating future fields and sensor trajectories from historical observations.","excerpt":"formulates this problem as the joint generation of future fields and sensor trajectories conditioned on historical observations","url":"https://arxiv.org/abs/2611.06280","relationship":"Source-stated"},{"text":"DyPSI combines Sensor Position Fields, a shared Functional Tucker representation, and a joint diffusion model.","excerpt":"encodes fields and sensing configurations with a shared Functional Tucker representation, and trains a joint diffusion model","url":"https://arxiv.org/abs/2611.06280","relationship":"Source-stated"},{"text":"The authors report that DyPSI outperforms static and frame-wise placement strategies in three stated experiment domains, with substantial error reduction under tight sensor budgets.","excerpt":"Experiments on turbulent flow, GLORYS12 sea-surface temperature, and 3D car aerodynamics show that DyPSI consistently outperforms static and frame-wise placement strategies, with substantial error reduction under tight sensor budgets.","url":"https://arxiv.org/abs/2611.06280","relationship":"Source-stated"}],"sourceMedia":{"status":"not_found","sourceUrl":"https://arxiv.org/abs/2611.06280","checkedAt":"2026-11-09T10:38:10.238Z","items":[],"note":"No suitable media was found by the supported page extraction."},"assumptions":[],"openQuestion":{"attribution":"Open question","text":"How large were the reported error reductions under tight sensor budgets in each of the three experiments?"},"whatWouldChangeView":{"attribution":"Change condition not recorded","text":""},"editorialComparisons":[],"live":{"title":"DyPSI: Dynamic Physics Sensing via Joint Field and Sensor-Trajectory Generation","summary":"The paper’s authors introduce DyPSI, a framework that reconstructs changing physical fields from sparse observations while deciding where sensors should collect future observations. They argue that methods using one sensor layout across all frames, or choosing placements independently frame by frame, can limit temporally coherent sensing trajectories.\n\nDyPSI generates future fields and sensor trajectories together, conditioned on historical observations. It represents sensor coordinates as Sensor Position Fields, encodes fields and sensing configurations with a shared Functional Tucker representation, and trains a joint diffusion model. A Gaussian-process temporal kernel correlates perturbations during training, biasing the denoiser toward temporally smooth recoveries. The authors also link the EDM training objective to an A-optimality criterion and provide a bound on discrete trajectory variation.\n\nIn experiments on turbulent flow, GLORYS12 sea-surface temperature, and 3D car aerodynamics, the authors report that DyPSI outperforms static and frame-wise placement strategies, with substantial error reduction under tight sensor budgets. The abstract does not provide numerical effect sizes, and the reported experiments do not establish performance in other settings.","question":"How large were the reported error reductions under tight sensor budgets in each of the three experiments?","claims":[{"text":"The authors present DyPSI as jointly generating future fields and sensor trajectories from historical observations.","excerpt":"formulates this problem as the joint generation of future fields and sensor trajectories conditioned on historical observations","support":"source-stated"},{"text":"DyPSI combines Sensor Position Fields, a shared Functional Tucker representation, and a joint diffusion model.","excerpt":"encodes fields and sensing configurations with a shared Functional Tucker representation, and trains a joint diffusion model","support":"source-stated"},{"text":"The authors report that DyPSI outperforms static and frame-wise placement strategies in three stated experiment domains, with substantial error reduction under tight sensor budgets.","excerpt":"Experiments on turbulent flow, GLORYS12 sea-surface temperature, and 3D car aerodynamics show that DyPSI consistently outperforms static and frame-wise placement strategies, with substantial error reduction under tight sensor budgets.","support":"source-stated"}],"limitations":["The supplied evidence presents the paper’s abstract, not detailed experimental results; it gives no numerical effect sizes for the reported error reductions.","The abstract reports results for turbulent flow, GLORYS12 sea-surface temperature, and 3D car aerodynamics; it does not establish performance in other settings."],"mediaId":"28929f1b89f93f522b0964260be4a07e5eee820477109b884ea07f1df87ac448","correctionReason":null,"id":"535b19ed1c1399b5eb021c3137b61520371c9a2f7aa003b6fdb2d36bac466efb","publicationId":"eeba1566-eb29-48dc-8d7e-ab04ca8ba691","version":1,"sourceUrl":"https://arxiv.org/abs/2611.06280","sourceTitle":"DyPSI: Dynamic Physics Sensing via Joint Field and Sensor-Trajectory Generation","publisher":"arXiv cs.AI","publishedAt":null,"originalPublished":{"value":"2026-11-04","precision":"day","basis":"publisher_citation_date"},"firstObservedAt":"2026-11-05T05:00:36.713Z","createdAt":"2026-11-09T09:34:35.122Z","historical":true,"entityIds":["arxiv"],"topicIds":["intelligence-and-discovery"],"media":[{"id":"28929f1b89f93f522b0964260be4a07e5eee820477109b884ea07f1df87ac448","kind":"article","url":"https://arxiv.org/abs/2611.06280","sourceUrl":"https://arxiv.org/abs/2611.06280","label":"Read original","provenance":"source_link","verification":{"status":"verified","checkedAt":"2026-11-09T09:33:42.195Z","method":"acquired_source","contentType":"text/html"},"recommendationReason":"Recommended by Jev for understanding this source; checked by Luna."}],"sourceMedia":{"status":"not_found","sourceUrl":"https://arxiv.org/abs/2611.06280","checkedAt":"2026-11-09T10:38:10.238Z","items":[],"note":"No suitable media was found by the supported page extraction."},"editorialDecision":{"outcome":"accept","reasonCodes":["material_development"],"rubricVersion":"stream-admission-v5","mediaId":"28929f1b89f93f522b0964260be4a07e5eee820477109b884ea07f1df87ac448","confidence":0.78,"provider":"typesafe","model":"jev-1.13.0","inputHash":"29ee1acb6aa0240c1be79cdc128000647ed118a9292966bd95943b2e4e0eee9e","decidedAt":"2026-11-09T09:33:44.153Z"},"provenance":{"textHash":"5a0e73af9ae6e0117b9f76842223180915de4d58f4573a8d58dc516835c757c5","observationId":"42812e52b0be4e9042a6d2ba61d5ffdc52d1573d1c02fba99115c18e8e796dbf","retrievedAt":"2026-11-09T09:33:41.858Z","kind":"publisher-page","preparer":{"model":"gpt-6-luna","runId":"8277932f-f035-4f9e-831f-75fcf6ced081"},"reviewer":{"model":"gpt-6-luna","runId":"7910a7e1-113f-4aa1-8ec1-5793e4c312f3"}},"audit":{"status":"passed","reviewedAt":"2026-11-09T09:34:35.122Z","model":"anthropic/claude-opus-5.5"},"sourceAttribution":{"authors":[{"name":"Zhang, Yizhou","url":null,"kind":"unknown"},{"name":"Chen, Panqi","url":null,"kind":"unknown"},{"name":"Cheng, Lei","url":null,"kind":"unknown"},{"name":"Zhang, Ting","url":null,"kind":"unknown"},{"name":"Li, Jianlong","url":null,"kind":"unknown"},{"name":"Shi, Jianfeng","url":null,"kind":"unknown"},{"name":"Fang, Shikai","url":null,"kind":"unknown"}],"authorRole":"article-author","hosts":[],"status":"stated","authorRoster":{"truncated":false,"omitted":0,"assertionGroupsOmitted":0},"participation":[{"actor":{"name":"Zhang, Yizhou","url":null,"kind":"unknown","actorId":null},"role":"author","scope":{"kind":"work-credit","locator":null},"literalCredit":"Zhang, Yizhou","status":"stated","provenance":{"basis":"head-meta","sourceUrl":"https://arxiv.org/abs/2611.06280","locator":"head/meta[name=author|citation_author]"},"assertionId":"62184e9ebc0689465b64fafd9638d0f28f79f94a3d991bce2e78743cd8219253:participation:0","identity":{"version":"entity-identity-v1","status":"unresolved","actorId":null,"canonicalActorId":null,"candidateActorIds":[],"assertions":[],"sourceObservedAt":"2026-11-09T10:38:10.238Z","knowledgeAsOf":null},"sourceVersion":{"snapshotId":"62184e9ebc0689465b64fafd9638d0f28f79f94a3d991bce2e78743cd8219253","sourceUrl":"https://arxiv.org/abs/2611.06280","contentSha256":"3c02ee5687891710c09fe6c351069675d6a3a0bb61467479f4a38d2ed93c2545","textHash":"5a0e73af9ae6e0117b9f76842223180915de4d58f4573a8d58dc516835c757c5","observedAt":"2026-11-09T10:38:10.238Z"}},{"actor":{"name":"Chen, Panqi","url":null,"kind":"unknown","actorId":null},"role":"author","scope":{"kind":"work-credit","locator":null},"literalCredit":"Chen, Panqi","status":"stated","provenance":{"basis":"head-meta","sourceUrl":"https://arxiv.org/abs/2611.06280","locator":"head/meta[name=author|citation_author]"},"assertionId":"62184e9ebc0689465b64fafd9638d0f28f79f94a3d991bce2e78743cd8219253:participation:1","identity":{"version":"entity-identity-v1","status":"unresolved","actorId":null,"canonicalActorId":null,"candidateActorIds":[],"assertions":[],"sourceObservedAt":"2026-11-09T10:38:10.238Z","knowledgeAsOf":null},"sourceVersion":{"snapshotId":"62184e9ebc0689465b64fafd9638d0f28f79f94a3d991bce2e78743cd8219253","sourceUrl":"https://arxiv.org/abs/2611.06280","contentSha256":"3c02ee5687891710c09fe6c351069675d6a3a0bb61467479f4a38d2ed93c2545","textHash":"5a0e73af9ae6e0117b9f76842223180915de4d58f4573a8d58dc516835c757c5","observedAt":"2026-11-09T10:38:10.238Z"}},{"actor":{"name":"Cheng, Lei","url":null,"kind":"unknown","actorId":null},"role":"author","scope":{"kind":"work-credit","locator":null},"literalCredit":"Cheng, Lei","status":"stated","provenance":{"basis":"head-meta","sourceUrl":"https://arxiv.org/abs/2611.06280","locator":"head/meta[name=author|citation_author]"},"assertionId":"62184e9ebc0689465b64fafd9638d0f28f79f94a3d991bce2e78743cd8219253:participation:2","identity":{"version":"entity-identity-v1","status":"unresolved","actorId":null,"canonicalActorId":null,"candidateActorIds":[],"assertions":[],"sourceObservedAt":"2026-11-09T10:38:10.238Z","knowledgeAsOf":null},"sourceVersion":{"snapshotId":"62184e9ebc0689465b64fafd9638d0f28f79f94a3d991bce2e78743cd8219253","sourceUrl":"https://arxiv.org/abs/2611.06280","contentSha256":"3c02ee5687891710c09fe6c351069675d6a3a0bb61467479f4a38d2ed93c2545","textHash":"5a0e73af9ae6e0117b9f76842223180915de4d58f4573a8d58dc516835c757c5","observedAt":"2026-11-09T10:38:10.238Z"}},{"actor":{"name":"Zhang, Ting","url":null,"kind":"unknown","actorId":null},"role":"author","scope":{"kind":"work-credit","locator":null},"literalCredit":"Zhang, Ting","status":"stated","provenance":{"basis":"head-meta","sourceUrl":"https://arxiv.org/abs/2611.06280","locator":"head/meta[name=author|citation_author]"},"assertionId":"62184e9ebc0689465b64fafd9638d0f28f79f94a3d991bce2e78743cd8219253:participation:3","identity":{"version":"entity-identity-v1","status":"unresolved","actorId":null,"canonicalActorId":null,"candidateActorIds":[],"assertions":[],"sourceObservedAt":"2026-11-09T10:38:10.238Z","knowledgeAsOf":null},"sourceVersion":{"snapshotId":"62184e9ebc0689465b64fafd9638d0f28f79f94a3d991bce2e78743cd8219253","sourceUrl":"https://arxiv.org/abs/2611.06280","contentSha256":"3c02ee5687891710c09fe6c351069675d6a3a0bb61467479f4a38d2ed93c2545","textHash":"5a0e73af9ae6e0117b9f76842223180915de4d58f4573a8d58dc516835c757c5","observedAt":"2026-11-09T10:38:10.238Z"}},{"actor":{"name":"Li, Jianlong","url":null,"kind":"unknown","actorId":null},"role":"author","scope":{"kind":"work-credit","locator":null},"literalCredit":"Li, Jianlong","status":"stated","provenance":{"basis":"head-meta","sourceUrl":"https://arxiv.org/abs/2611.06280","locator":"head/meta[name=author|citation_author]"},"assertionId":"62184e9ebc0689465b64fafd9638d0f28f79f94a3d991bce2e78743cd8219253:participation:4","identity":{"version":"entity-identity-v1","status":"unresolved","actorId":null,"canonicalActorId":null,"candidateActorIds":[],"assertions":[],"sourceObservedAt":"2026-11-09T10:38:10.238Z","knowledgeAsOf":null},"sourceVersion":{"snapshotId":"62184e9ebc0689465b64fafd9638d0f28f79f94a3d991bce2e78743cd8219253","sourceUrl":"https://arxiv.org/abs/2611.06280","contentSha256":"3c02ee5687891710c09fe6c351069675d6a3a0bb61467479f4a38d2ed93c2545","textHash":"5a0e73af9ae6e0117b9f76842223180915de4d58f4573a8d58dc516835c757c5","observedAt":"2026-11-09T10:38:10.238Z"}},{"actor":{"name":"Shi, Jianfeng","url":null,"kind":"unknown","actorId":null},"role":"author","scope":{"kind":"work-credit","locator":null},"literalCredit":"Shi, Jianfeng","status":"stated","provenance":{"basis":"head-meta","sourceUrl":"https://arxiv.org/abs/2611.06280","locator":"head/meta[name=author|citation_author]"},"assertionId":"62184e9ebc0689465b64fafd9638d0f28f79f94a3d991bce2e78743cd8219253:participation:5","identity":{"version":"entity-identity-v1","status":"unresolved","actorId":null,"canonicalActorId":null,"candidateActorIds":[],"assertions":[],"sourceObservedAt":"2026-11-09T10:38:10.238Z","knowledgeAsOf":null},"sourceVersion":{"snapshotId":"62184e9ebc0689465b64fafd9638d0f28f79f94a3d991bce2e78743cd8219253","sourceUrl":"https://arxiv.org/abs/2611.06280","contentSha256":"3c02ee5687891710c09fe6c351069675d6a3a0bb61467479f4a38d2ed93c2545","textHash":"5a0e73af9ae6e0117b9f76842223180915de4d58f4573a8d58dc516835c757c5","observedAt":"2026-11-09T10:38:10.238Z"}},{"actor":{"name":"Fang, Shikai","url":null,"kind":"unknown","actorId":null},"role":"author","scope":{"kind":"work-credit","locator":null},"literalCredit":"Fang, Shikai","status":"stated","provenance":{"basis":"head-meta","sourceUrl":"https://arxiv.org/abs/2611.06280","locator":"head/meta[name=author|citation_author]"},"assertionId":"62184e9ebc0689465b64fafd9638d0f28f79f94a3d991bce2e78743cd8219253:participation:6","identity":{"version":"entity-identity-v1","status":"unresolved","actorId":null,"canonicalActorId":null,"candidateActorIds":[],"assertions":[],"sourceObservedAt":"2026-11-09T10:38:10.238Z","knowledgeAsOf":null},"sourceVersion":{"snapshotId":"62184e9ebc0689465b64fafd9638d0f28f79f94a3d991bce2e78743cd8219253","sourceUrl":"https://arxiv.org/abs/2611.06280","contentSha256":"3c02ee5687891710c09fe6c351069675d6a3a0bb61467479f4a38d2ed93c2545","textHash":"5a0e73af9ae6e0117b9f76842223180915de4d58f4573a8d58dc516835c757c5","observedAt":"2026-11-09T10:38:10.238Z"}}],"participationCoverage":{"total":7,"returned":7,"complete":true,"fullAssertions":"source-metadata-endpoint"},"distribution":null,"publisher":null,"sourceUrl":"https://arxiv.org/abs/2611.06280","snapshotId":"62184e9ebc0689465b64fafd9638d0f28f79f94a3d991bce2e78743cd8219253","contentSha256":"3c02ee5687891710c09fe6c351069675d6a3a0bb61467479f4a38d2ed93c2545","textHash":"5a0e73af9ae6e0117b9f76842223180915de4d58f4573a8d58dc516835c757c5","observedAt":"2026-11-09T10:38:10.238Z","basis":"publisher-page-metadata","sourceLinks":46,"linksTruncated":false,"metadataUrl":"/api/pipeline/stream/535b19ed1c1399b5eb021c3137b61520371c9a2f7aa003b6fdb2d36bac466efb/source"}}}],"audits":[{"publicationId":"eeba1566-eb29-48dc-8d7e-ab04ca8ba691","verdict":"passed","findings":[{"dimension":"attribution","status":"pass","explanation":"The reading attributes DyPSI, its framing and its results to the paper's authors. Performance claims are kept as reported claims ('the authors report'), not presented as established fact.","excerpt":"Authors:Yizhou Zhang, Panqi Chen, Lei Cheng, Ting Zhang, Jianlong Li, Jianfeng Shi, Shikai Fang"},{"dimension":"summary","status":"pass","explanation":"The summary follows the abstract: it states the problem, the critique of static and frame-wise sensor layouts, the method, the analysis and the three experiment domains. The opening paragraph explains the core idea in plain language, and the examples make 'fields' concrete. Technical terms such as Functional Tucker, EDM and A-optimality are kept exactly and suit tooltips. Shortening 'sequence-aggregated A-optimality criterion' to 'an A-optimality criterion' loses minor precision but does not change the meaning. The question about effect sizes follows from a real gap in the evidence.","excerpt":"Physics sensing in evolving environments requires reconstructing dense spatiotemporal fields from sparse observations while adapting where future observations should be collected."},{"dimension":"date","status":"pass","explanation":"The originalPublished date of 2026-11-04 matches the v1 submission date on arXiv. The feed date of 2026-11-05 is correctly not used.","excerpt":"[Submitted on 4 Nov 2026]"},{"dimension":"support","status":"pass","explanation":"Each claim matches an abstract excerpt. The limitations about missing numerical effect sizes and the lack of evidence beyond the three domains are accurate and do not overreach. The reading implies no unsupported benefits or costs.","excerpt":"show that DyPSI consistently outperforms static and frame-wise placement strategies, with substantial error reduction under tight sensor budgets"},{"dimension":"media","status":"pass","explanation":"The selected media is the verified arXiv abstract page for this paper. It is the direct original source.","excerpt":null}],"createdAt":"2026-11-09T09:34:35.122Z","model":"anthropic/claude-opus-5.5"}],"correctionUrl":"https://escapemeridian.com/corrections/"}