Paper Citation Record · LEDGER

Data-driven surrogate models for forecasting experimentally measured fluid flows

As of 10 August 2026, this Paper Citation Record lists 58 of 58 outbound references and 0 inbound Pith citation observations for arXiv:2606.10848.

A citation records a reference. It does not transfer a finding from one paper to another.

pith.paper-citation-record.v1
2606.10848 v2

Coverage vector

measured 58 of 58 reference resolution

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Source: paper_references, paper_reference_links, observed 2026-06-27T11:52:13.989162Z

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Reference resolution

58 of 58 outbound references displayed

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Outbound references

Observation 79ed54c1-458f-4fd2-b864-4bfe632a4f9c · outbound

This paper cites An acrylic cylinder of diameterD= 9.53×10 −3 m was mounted to span the walls of the test section approximately equidistant from the free surface and the tunnel floor.

Data-driven surrogate models for forecasting experimentally measured fluid flows An acrylic cylinder of diameterD= 9.53×10 −3 m was mounted to span the walls of the test section approximately equidistant from the free surface and the tunnel floor

Reference 1

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Observation 15669e6e-ce78-475d-b383-650a6dd92e67 · outbound

This paper cites encoder-decoder.

Data-driven surrogate models for forecasting experimentally measured fluid flows encoder-decoder

Reference 2

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Data-driven surrogate models for forecasting experimentally measured fluid flows Unresolved cited work

Reference 3

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This paper cites an unresolved cited work.

Data-driven surrogate models for forecasting experimentally measured fluid flows Unresolved cited work

Reference 4

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Observation df3f53bc-d853-40a0-b843-38b8e0b64608 · outbound

This paper cites A departure from the network-based deep learning methods discussed above, DMD allows for the development of reduced-order linear models of nonlin- ear dynamical systems.

Data-driven surrogate models for forecasting experimentally measured fluid flows A departure from the network-based deep learning methods discussed above, DMD allows for the development of reduced-order linear models of nonlin- ear dynamical systems

Reference 5

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Observation 75fd9887-af78-483e-a6ed-ea0f4fceab1f · outbound

This paper cites The velocity fields are predicted in increments of non-dimensional time approximately equal tot ∗ ≈0.18.

Data-driven surrogate models for forecasting experimentally measured fluid flows The velocity fields are predicted in increments of non-dimensional time approximately equal tot ∗ ≈0.18

Reference 6

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Observation f264ab64-8c4d-4f8c-89f0-82c5da0a2958 · outbound

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Data-driven surrogate models for forecasting experimentally measured fluid flows Unresolved cited work

Reference 7

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Observation 0fd74472-bc56-4d56-b644-eda0e80598ad · outbound

This paper cites Srinivasan, L.

Data-driven surrogate models for forecasting experimentally measured fluid flows Srinivasan, L

Reference 8

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Observation eb0dac49-455c-4df1-98b0-f0ee7368137b · outbound

This paper cites Nakamura, K.

Data-driven surrogate models for forecasting experimentally measured fluid flows Nakamura, K

Reference 9

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Data-driven surrogate models for forecasting experimentally measured fluid flows Unresolved cited work

Reference 10

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Observation a738243e-f981-4bb7-a2c3-19ba24ca608c · outbound

This paper cites Fukami, T.

Data-driven surrogate models for forecasting experimentally measured fluid flows Fukami, T

Reference 11

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Data-driven surrogate models for forecasting experimentally measured fluid flows Unresolved cited work

Reference 12

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Observation 7437c435-e7d1-4ef5-bbe8-f2b7239384f2 · outbound

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Data-driven surrogate models for forecasting experimentally measured fluid flows Unresolved cited work

Reference 13

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Observation 3d0ff3a1-4ab0-4adc-966c-8537672e02a8 · outbound

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Data-driven surrogate models for forecasting experimentally measured fluid flows Unresolved cited work

Reference 14

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Data-driven surrogate models for forecasting experimentally measured fluid flows Unresolved cited work

Reference 15

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Data-driven surrogate models for forecasting experimentally measured fluid flows Unresolved cited work

Reference 16

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Observation 4cc40764-8286-44ec-a429-9948d511bd89 · outbound

This paper cites Williamson, Vortex dynamics in the cylinder wake, Annual Review of Fluid Mechanics (1996).

Data-driven surrogate models for forecasting experimentally measured fluid flows Williamson, Vortex dynamics in the cylinder wake, Annual Review of Fluid Mechanics (1996)

Reference 17

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Observation c5717bef-afbc-459b-b56d-dc47a88c04b8 · outbound

This paper cites Hasegawa, K.

Data-driven surrogate models for forecasting experimentally measured fluid flows Hasegawa, K

Reference 18

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Observation 8d66a1dc-2577-458d-aeb9-803dc635b807 · outbound

This paper cites Hasegawa, K.

Data-driven surrogate models for forecasting experimentally measured fluid flows Hasegawa, K

Reference 19

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Observation 16e49fe4-f1f8-4744-8a32-125e1607fdce · outbound

This paper cites Moriomoto, K.

Data-driven surrogate models for forecasting experimentally measured fluid flows Moriomoto, K

Reference 20

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Observation 17996577-6424-437c-a0fb-41a7b4cfec31 · outbound

This paper cites Williamson, Three-dimensional wake transition, Journal of Fluid Mechanics (1996).

Data-driven surrogate models for forecasting experimentally measured fluid flows Williamson, Three-dimensional wake transition, Journal of Fluid Mechanics (1996)

Reference 21

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Observation 3298e842-d850-4031-a18a-af6dc6005610 · outbound

This paper cites Roshko,Report 1191: On the Development of Turbulent Wakes from Vortex Streets, Tech.

Data-driven surrogate models for forecasting experimentally measured fluid flows Roshko,Report 1191: On the Development of Turbulent Wakes from Vortex Streets, Tech

Reference 22

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Observation 400705de-0c1c-4b17-bf33-9ea436a86f86 · outbound

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Data-driven surrogate models for forecasting experimentally measured fluid flows Jeon and M

Reference 23

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Data-driven surrogate models for forecasting experimentally measured fluid flows Unresolved cited work

Reference 24

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Observation 333eb3a5-4bf9-4a11-9d6d-43b27a840a62 · outbound

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Data-driven surrogate models for forecasting experimentally measured fluid flows Hammache and M

Reference 25

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Observation 660595d5-ba57-4578-89d4-3902d881baa9 · outbound

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Data-driven surrogate models for forecasting experimentally measured fluid flows Prasad and C

Reference 26

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Observation f44d4869-50e3-4e00-934e-2f0daf20562c · outbound

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Data-driven surrogate models for forecasting experimentally measured fluid flows Prasad and C

Reference 27

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Observation 7c2c41e5-5407-4514-8438-2a2994220ab9 · outbound

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Data-driven surrogate models for forecasting experimentally measured fluid flows Reynolds and A

Reference 28

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Observation 37669ff6-8ace-4084-98ff-ba7ca1962b91 · outbound

This paper cites Berkooz, P.

Data-driven surrogate models for forecasting experimentally measured fluid flows Berkooz, P

Reference 29

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Data-driven surrogate models for forecasting experimentally measured fluid flows Perrin, M

Reference 30

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Data-driven surrogate models for forecasting experimentally measured fluid flows Unresolved cited work

Reference 31

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Observation ad1eacdc-6c13-484f-bbe9-7c92a09262f5 · outbound

This paper cites Achermann, T.

Data-driven surrogate models for forecasting experimentally measured fluid flows Achermann, T

Reference 32

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Data-driven surrogate models for forecasting experimentally measured fluid flows Unresolved cited work

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Data-driven surrogate models for forecasting experimentally measured fluid flows Pan and K

Reference 34

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Data-driven surrogate models for forecasting experimentally measured fluid flows Unresolved cited work

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Observation 4169e9ec-3f0c-4e2b-abba-6c7250848a28 · outbound

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Data-driven surrogate models for forecasting experimentally measured fluid flows Ronneberger, P

Reference 36

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Observation 9c362ea7-7d25-4a5c-b117-8459155c2031 · outbound

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Data-driven surrogate models for forecasting experimentally measured fluid flows Kovachki, Z

Reference 37

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Data-driven surrogate models for forecasting experimentally measured fluid flows Kovachki, S

Reference 38

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Data-driven surrogate models for forecasting experimentally measured fluid flows Unresolved cited work

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Data-driven surrogate models for forecasting experimentally measured fluid flows H´ eas and C

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Data-driven surrogate models for forecasting experimentally measured fluid flows Unresolved cited work

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Observation 0e2a5c57-10e6-453b-8bfd-d7942e914e59 · outbound

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Data-driven surrogate models for forecasting experimentally measured fluid flows Rahaman, A

Reference 42

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Data-driven surrogate models for forecasting experimentally measured fluid flows Unresolved cited work

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Data-driven surrogate models for forecasting experimentally measured fluid flows Unresolved cited work

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Data-driven surrogate models for forecasting experimentally measured fluid flows Oommenn, S

Reference 45

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This paper cites Physics-Informed Neural Operator for Learning Partial Differential Equations.

Data-driven surrogate models for forecasting experimentally measured fluid flows Physics-Informed Neural Operator for Learning Partial Differential Equations

Reference 46

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Observation e9045d7c-1bcd-43e1-ad86-bd88f270518d · outbound

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Data-driven surrogate models for forecasting experimentally measured fluid flows Unresolved cited work

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Data-driven surrogate models for forecasting experimentally measured fluid flows Willert, M

Reference 48

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Data-driven surrogate models for forecasting experimentally measured fluid flows Kreizer, D

Reference 49

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Data-driven surrogate models for forecasting experimentally measured fluid flows Varon, J.-L

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Data-driven surrogate models for forecasting experimentally measured fluid flows Unresolved cited work

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Observation 42b8e08a-0055-43ce-9b3c-e4c49ad5bfe6 · outbound

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Data-driven surrogate models for forecasting experimentally measured fluid flows Bright, G

Reference 52

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Data-driven surrogate models for forecasting experimentally measured fluid flows Unresolved cited work

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Observation 4122103b-7f7c-4310-a798-346457d765f5 · outbound

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Data-driven surrogate models for forecasting experimentally measured fluid flows Loiseau, B

Reference 54

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Observation 50623128-70c3-4117-92b3-b800c79260ed · outbound

This paper cites Fukami, B.

Data-driven surrogate models for forecasting experimentally measured fluid flows Fukami, B

Reference 55

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Observation 3638a703-45ae-46c0-bbe1-6ad98c4c7c90 · outbound

This paper cites Fukami, R.

Data-driven surrogate models for forecasting experimentally measured fluid flows Fukami, R

Reference 56

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Observation eaa267df-4b43-40a8-a1f6-a349308ae230 · outbound

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Data-driven surrogate models for forecasting experimentally measured fluid flows Unresolved cited work

Reference 57

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Data-driven surrogate models for forecasting experimentally measured fluid flows Unresolved cited work

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