Typed states for the displayed outbound observations.
Source: paper_references, paper_reference_links, observed 2026-08-12T17:08:12.856730Z
Paper Citation Record · LEDGER
As of 14 August 2026, this Paper Citation Record lists 65 of 65 outbound references and 0 inbound Pith citation observations for arXiv:2411.12940.
A citation records a reference. It does not transfer a finding from one paper to another.
Typed states for the displayed outbound observations.
Source: paper_references, paper_reference_links, observed 2026-08-12T17:08:12.856730Z
One-hop event checks from named stored sources.
Source: scholarly_work_events, retraction_status_cache, observed 2026-08-14T06:32:32.682623+00:00
Pith citing papers itemized under the disclosed page cap.
Source: paper_references, paper_reference_links
A source-named dated measurement, never combined with another source.
Source: cited_works
65 of 65 outbound references displayed
External citation measurements
No source-named external measurement is stored.
Observation 9e445512-40ec-4e3d-972d-59237ef456c4 · outbound
On the relationship between Koopman operator approximations and neural ordinary differential equations for data-driven time-evolution predictions Unresolved cited work
Reference 1
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Observation 73b1108c-9723-4939-ad69-942011fc9004 · outbound
On the relationship between Koopman operator approximations and neural ordinary differential equations for data-driven time-evolution predictions Budišić , author R
Reference 3
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On the relationship between Koopman operator approximations and neural ordinary differential equations for data-driven time-evolution predictions Unresolved cited work
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On the relationship between Koopman operator approximations and neural ordinary differential equations for data-driven time-evolution predictions Unresolved cited work
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On the relationship between Koopman operator approximations and neural ordinary differential equations for data-driven time-evolution predictions Unresolved cited work
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On the relationship between Koopman operator approximations and neural ordinary differential equations for data-driven time-evolution predictions Unresolved cited work
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On the relationship between Koopman operator approximations and neural ordinary differential equations for data-driven time-evolution predictions Unresolved cited work
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On the relationship between Koopman operator approximations and neural ordinary differential equations for data-driven time-evolution predictions Unresolved cited work
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On the relationship between Koopman operator approximations and neural ordinary differential equations for data-driven time-evolution predictions Unresolved cited work
Reference 10
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Observation 6423e3b2-c348-4f39-ad9b-7db05d182c03 · outbound
On the relationship between Koopman operator approximations and neural ordinary differential equations for data-driven time-evolution predictions Unresolved cited work
Reference 11
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Reference 12
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On the relationship between Koopman operator approximations and neural ordinary differential equations for data-driven time-evolution predictions The Multiverse of Dynamic Mode Decomposition Algorithms
Reference 13
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On the relationship between Koopman operator approximations and neural ordinary differential equations for data-driven time-evolution predictions Unresolved cited work
Reference 14
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On the relationship between Koopman operator approximations and neural ordinary differential equations for data-driven time-evolution predictions Unresolved cited work
Reference 15
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On the relationship between Koopman operator approximations and neural ordinary differential equations for data-driven time-evolution predictions Unresolved cited work
Reference 16
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Observation 08dd8747-b0d8-4dec-a918-267916b2e981 · outbound
On the relationship between Koopman operator approximations and neural ordinary differential equations for data-driven time-evolution predictions Lusch , author J
Reference 17
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On the relationship between Koopman operator approximations and neural ordinary differential equations for data-driven time-evolution predictions Unresolved cited work
Reference 18
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On the relationship between Koopman operator approximations and neural ordinary differential equations for data-driven time-evolution predictions Unresolved cited work
Reference 19
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On the relationship between Koopman operator approximations and neural ordinary differential equations for data-driven time-evolution predictions Eivazi , author L
Reference 20
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Observation 6b36bcdb-33f4-4201-9d66-8e71b41ab02d · outbound
On the relationship between Koopman operator approximations and neural ordinary differential equations for data-driven time-evolution predictions Junker , author J
Reference 21
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Observation d4836fbd-349b-4fe1-8679-4c4b4d3be691 · outbound
On the relationship between Koopman operator approximations and neural ordinary differential equations for data-driven time-evolution predictions Van Goor , author R
Reference 22
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Observation 0e75164c-2ad8-405e-aae7-20faabfeb986 · outbound
On the relationship between Koopman operator approximations and neural ordinary differential equations for data-driven time-evolution predictions Leveraging KANs For Enhanced Deep Koopman Operator Discovery
Reference 23
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Observation 0a0ffc70-9018-44ee-83e9-526742f17f0f · outbound
On the relationship between Koopman operator approximations and neural ordinary differential equations for data-driven time-evolution predictions Rico-Martinez , author K
Reference 24
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On the relationship between Koopman operator approximations and neural ordinary differential equations for data-driven time-evolution predictions Rico-Martinez , author J
Reference 25
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Observation d4dd140b-7ec3-413f-b472-a82fce5e2bd3 · outbound
On the relationship between Koopman operator approximations and neural ordinary differential equations for data-driven time-evolution predictions Rico-Martinez \ and\ author I
Reference 26
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On the relationship between Koopman operator approximations and neural ordinary differential equations for data-driven time-evolution predictions Unresolved cited work
Reference 27
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On the relationship between Koopman operator approximations and neural ordinary differential equations for data-driven time-evolution predictions Unresolved cited work
Reference 28
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On the relationship between Koopman operator approximations and neural ordinary differential equations for data-driven time-evolution predictions Unresolved cited work
Reference 29
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Observation 996030aa-cf39-463d-b2cd-ed9bb4c6725b · outbound
On the relationship between Koopman operator approximations and neural ordinary differential equations for data-driven time-evolution predictions Lasota \ and\ author M
Reference 30
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Observation bca866b6-87e1-4704-96d8-c5412c4dee2c · outbound
On the relationship between Koopman operator approximations and neural ordinary differential equations for data-driven time-evolution predictions Abraham , author J
Reference 31
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Observation cb4a0ccc-e5db-4d0a-a13e-364af3054e7a · outbound
On the relationship between Koopman operator approximations and neural ordinary differential equations for data-driven time-evolution predictions Cvitanović , author R
Reference 32
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Observation 8c5f3902-5a42-4b2f-9215-a991358fa256 · outbound
On the relationship between Koopman operator approximations and neural ordinary differential equations for data-driven time-evolution predictions Mauroy \ and\ author J
Reference 33
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Observation 76714b76-417c-4b61-ac28-c94c2f29dd5c · outbound
On the relationship between Koopman operator approximations and neural ordinary differential equations for data-driven time-evolution predictions Korda \ and\ author I
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On the relationship between Koopman operator approximations and neural ordinary differential equations for data-driven time-evolution predictions Unresolved cited work
Reference 36
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On the relationship between Koopman operator approximations and neural ordinary differential equations for data-driven time-evolution predictions Kidger , author J
Reference 37
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On the relationship between Koopman operator approximations and neural ordinary differential equations for data-driven time-evolution predictions On Neural Differential Equations
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On the relationship between Koopman operator approximations and neural ordinary differential equations for data-driven time-evolution predictions Finlay , author J.-H
Reference 39
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On the relationship between Koopman operator approximations and neural ordinary differential equations for data-driven time-evolution predictions Kelly , author J
Reference 40
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On the relationship between Koopman operator approximations and neural ordinary differential equations for data-driven time-evolution predictions Unresolved cited work
Reference 41
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On the relationship between Koopman operator approximations and neural ordinary differential equations for data-driven time-evolution predictions Unresolved cited work
Reference 42
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On the relationship between Koopman operator approximations and neural ordinary differential equations for data-driven time-evolution predictions Ansel , author E
Reference 43
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On the relationship between Koopman operator approximations and neural ordinary differential equations for data-driven time-evolution predictions Folkestad , author S
Reference 44
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On the relationship between Koopman operator approximations and neural ordinary differential equations for data-driven time-evolution predictions Korda \ and\ author I
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On the relationship between Koopman operator approximations and neural ordinary differential equations for data-driven time-evolution predictions Data-driven Koopman operator predictions of turbulent dynamics in models of shear flows
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On the relationship between Koopman operator approximations and neural ordinary differential equations for data-driven time-evolution predictions Mauroy \ and\ author J
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On the relationship between Koopman operator approximations and neural ordinary differential equations for data-driven time-evolution predictions Peitz , author S
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On the relationship between Koopman operator approximations and neural ordinary differential equations for data-driven time-evolution predictions Modularized data-driven approximation of the Koopman operator and generator
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On the relationship between Koopman operator approximations and neural ordinary differential equations for data-driven time-evolution predictions Racca \ and\ author L
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On the relationship between Koopman operator approximations and neural ordinary differential equations for data-driven time-evolution predictions Unresolved cited work
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On the relationship between Koopman operator approximations and neural ordinary differential equations for data-driven time-evolution predictions Unresolved cited work
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On the relationship between Koopman operator approximations and neural ordinary differential equations for data-driven time-evolution predictions Unresolved cited work
Reference 54
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On the relationship between Koopman operator approximations and neural ordinary differential equations for data-driven time-evolution predictions Unresolved cited work
Reference 55
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On the relationship between Koopman operator approximations and neural ordinary differential equations for data-driven time-evolution predictions Moehlis , author H
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On the relationship between Koopman operator approximations and neural ordinary differential equations for data-driven time-evolution predictions Moehlis , author H
Reference 57
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On the relationship between Koopman operator approximations and neural ordinary differential equations for data-driven time-evolution predictions Viswanath ,\ @noop title Lyapunov exponents from random Fibonacci sequences to the Lorenz equations \ ( publisher Cornell University ,\ year 1998 ) NoStop
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On the relationship between Koopman operator approximations and neural ordinary differential equations for data-driven time-evolution predictions Kaiser , author J
Reference 59
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On the relationship between Koopman operator approximations and neural ordinary differential equations for data-driven time-evolution predictions Page \ and\ author R
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On the relationship between Koopman operator approximations and neural ordinary differential equations for data-driven time-evolution predictions Cenedese , author J
Reference 61
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On the relationship between Koopman operator approximations and neural ordinary differential equations for data-driven time-evolution predictions Haller \ and\ author B
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On the relationship between Koopman operator approximations and neural ordinary differential equations for data-driven time-evolution predictions Unresolved cited work
Reference 63
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On the relationship between Koopman operator approximations and neural ordinary differential equations for data-driven time-evolution predictions Peyr \'e , author M
Reference 64
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On the relationship between Koopman operator approximations and neural ordinary differential equations for data-driven time-evolution predictions Flamary , author N
Reference 65
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No inbound Pith citation observations are available.