Data-driven reconstruction of dynamical systems using Takens' Theorem, manifold learning, and universal function approximators | Paper Citation Record

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Data-driven reconstruction of dynamical systems using Takens' Theorem, manifold learning, and universal function approximators Unresolved cited work

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Data-driven reconstruction of dynamical systems using Takens' Theorem, manifold learning, and universal function approximators Unresolved cited work

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Data-driven reconstruction of dynamical systems using Takens' Theorem, manifold learning, and universal function approximators Unresolved cited work

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Data-driven reconstruction of dynamical systems using Takens' Theorem, manifold learning, and universal function approximators Berry and S

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Data-driven reconstruction of dynamical systems using Takens' Theorem, manifold learning, and universal function approximators Unresolved cited work

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This paper cites Takens, Detecting strange attractors in turbulence, inDy- namical Systems and Turbulence, Warwick 1980, Lecture Notes in Mathematics, V ol.

Data-driven reconstruction of dynamical systems using Takens' Theorem, manifold learning, and universal function approximators Takens, Detecting strange attractors in turbulence, inDy- namical Systems and Turbulence, Warwick 1980, Lecture Notes in Mathematics, V ol

Reference 8

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Observation 4566ecac-6351-4e33-932a-53bb2103e306 · outbound

This paper cites Stark, Delay embeddings for forced systems.

Data-driven reconstruction of dynamical systems using Takens' Theorem, manifold learning, and universal function approximators Stark, Delay embeddings for forced systems

Reference 10

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Data-driven reconstruction of dynamical systems using Takens' Theorem, manifold learning, and universal function approximators Unresolved cited work

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This paper cites Stark, D.

Data-driven reconstruction of dynamical systems using Takens' Theorem, manifold learning, and universal function approximators Stark, D

Reference 12

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This paper cites Bara ´nski, Y.

Data-driven reconstruction of dynamical systems using Takens' Theorem, manifold learning, and universal function approximators Bara ´nski, Y

Reference 13

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Data-driven reconstruction of dynamical systems using Takens' Theorem, manifold learning, and universal function approximators Unresolved cited work

Reference 14

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Observation 349dbc7d-3608-423f-9eeb-8513d9a2edca · outbound

This paper cites Wang and A.

Data-driven reconstruction of dynamical systems using Takens' Theorem, manifold learning, and universal function approximators Wang and A

Reference 15

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Data-driven reconstruction of dynamical systems using Takens' Theorem, manifold learning, and universal function approximators Wang and A

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This paper cites Topel and A.

Data-driven reconstruction of dynamical systems using Takens' Theorem, manifold learning, and universal function approximators Topel and A

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This paper cites Topel, A.

Data-driven reconstruction of dynamical systems using Takens' Theorem, manifold learning, and universal function approximators Topel, A

Reference 18

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This paper cites Kolmogorov-Sinai entropies identify optimal observables for prediction and dynamics reconstruction in chaotic systems.

Data-driven reconstruction of dynamical systems using Takens' Theorem, manifold learning, and universal function approximators Kolmogorov-Sinai entropies identify optimal observables for prediction and dynamics reconstruction in chaotic systems

Reference 19

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Data-driven reconstruction of dynamical systems using Takens' Theorem, manifold learning, and universal function approximators Unresolved cited work

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Observation 1a2d95e3-4cf3-4697-885f-2219865d8130 · outbound

This paper cites Brin and G.

Data-driven reconstruction of dynamical systems using Takens' Theorem, manifold learning, and universal function approximators Brin and G

Reference 21

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Data-driven reconstruction of dynamical systems using Takens' Theorem, manifold learning, and universal function approximators Unresolved cited work

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Data-driven reconstruction of dynamical systems using Takens' Theorem, manifold learning, and universal function approximators Unresolved cited work

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This paper cites Sauer, J.

Data-driven reconstruction of dynamical systems using Takens' Theorem, manifold learning, and universal function approximators Sauer, J

Reference 24

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Data-driven reconstruction of dynamical systems using Takens' Theorem, manifold learning, and universal function approximators Unresolved cited work

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Data-driven reconstruction of dynamical systems using Takens' Theorem, manifold learning, and universal function approximators Unresolved cited work

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Data-driven reconstruction of dynamical systems using Takens' Theorem, manifold learning, and universal function approximators Unresolved cited work

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Data-driven reconstruction of dynamical systems using Takens' Theorem, manifold learning, and universal function approximators Unresolved cited work

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Data-driven reconstruction of dynamical systems using Takens' Theorem, manifold learning, and universal function approximators Unresolved cited work

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Data-driven reconstruction of dynamical systems using Takens' Theorem, manifold learning, and universal function approximators Amadei, A

Reference 30

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Data-driven reconstruction of dynamical systems using Takens' Theorem, manifold learning, and universal function approximators Hegger, A

Reference 31

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Data-driven reconstruction of dynamical systems using Takens' Theorem, manifold learning, and universal function approximators Unresolved cited work

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Data-driven reconstruction of dynamical systems using Takens' Theorem, manifold learning, and universal function approximators Unresolved cited work

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This paper cites Whitney, Differentiable manifolds, Annals of Mathematics 37, 645 (1936).

Data-driven reconstruction of dynamical systems using Takens' Theorem, manifold learning, and universal function approximators Whitney, Differentiable manifolds, Annals of Mathematics 37, 645 (1936)

Reference 35

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Data-driven reconstruction of dynamical systems using Takens' Theorem, manifold learning, and universal function approximators Unresolved cited work

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Data-driven reconstruction of dynamical systems using Takens' Theorem, manifold learning, and universal function approximators Kantz and T

Reference 38

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Data-driven reconstruction of dynamical systems using Takens' Theorem, manifold learning, and universal function approximators Martin, C

Reference 39

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Data-driven reconstruction of dynamical systems using Takens' Theorem, manifold learning, and universal function approximators Scholz, M

Reference 40

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This paper cites Cao, Practical method for determining the minimum embed- ding dimension of a scalar time series, Physica D: Nonlinear Phenomena110, 43 (1997).

Data-driven reconstruction of dynamical systems using Takens' Theorem, manifold learning, and universal function approximators Cao, Practical method for determining the minimum embed- ding dimension of a scalar time series, Physica D: Nonlinear Phenomena110, 43 (1997)

Reference 41

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Data-driven reconstruction of dynamical systems using Takens' Theorem, manifold learning, and universal function approximators Nadler, S

Reference 42

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Data-driven reconstruction of dynamical systems using Takens' Theorem, manifold learning, and universal function approximators Unresolved cited work

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Data-driven reconstruction of dynamical systems using Takens' Theorem, manifold learning, and universal function approximators Nadler, S

Reference 44

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Data-driven reconstruction of dynamical systems using Takens' Theorem, manifold learning, and universal function approximators Unresolved cited work

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Data-driven reconstruction of dynamical systems using Takens' Theorem, manifold learning, and universal function approximators Unresolved cited work

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Data-driven reconstruction of dynamical systems using Takens' Theorem, manifold learning, and universal function approximators Unresolved cited work

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Data-driven reconstruction of dynamical systems using Takens' Theorem, manifold learning, and universal function approximators Salvador and P

Reference 48

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Data-driven reconstruction of dynamical systems using Takens' Theorem, manifold learning, and universal function approximators Williams and M

Reference 49

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Data-driven reconstruction of dynamical systems using Takens' Theorem, manifold learning, and universal function approximators Wang and A

Reference 50

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Data-driven reconstruction of dynamical systems using Takens' Theorem, manifold learning, and universal function approximators Unresolved cited work

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Data-driven reconstruction of dynamical systems using Takens' Theorem, manifold learning, and universal function approximators Unresolved cited work

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Data-driven reconstruction of dynamical systems using Takens' Theorem, manifold learning, and universal function approximators Unresolved cited work

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Observation 32ba9a15-95df-4af4-985e-3a5a948e2482 · outbound

This paper cites Sidky, W.

Data-driven reconstruction of dynamical systems using Takens' Theorem, manifold learning, and universal function approximators Sidky, W

Reference 55

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Data-driven reconstruction of dynamical systems using Takens' Theorem, manifold learning, and universal function approximators Unresolved cited work

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Observation 7a340da7-7fc3-4a63-9022-726593223e39 · outbound

This paper cites Scheffer, J.

Data-driven reconstruction of dynamical systems using Takens' Theorem, manifold learning, and universal function approximators Scheffer, J

Reference 57

Resolution: verified fuzzy; raw_fallback, observed 2026-08-08T12:26:38.910497Z

Source-reported events for the cited work

No event found in the named queried sources as of 2026-08-20T06:33:59.587034+00:00.

Observation 3c723f64-3f48-4ed2-9092-450f829f47af · outbound

This paper cites an unresolved cited work.

Data-driven reconstruction of dynamical systems using Takens' Theorem, manifold learning, and universal function approximators Unresolved cited work

Reference 58

Resolution: unresolved; raw_fallback, observed 2026-08-08T12:26:38.900505Z

Source-reported events for the cited work

No event found in the named queried sources as of 2026-08-20T06:33:59.587034+00:00.

Observation 78b402be-a078-495e-b050-61b21cd67d0c · outbound

This paper cites Haken,Synergetics: An Introduction(Springer Berlin Hei- delberg, 1983).

Data-driven reconstruction of dynamical systems using Takens' Theorem, manifold learning, and universal function approximators Haken,Synergetics: An Introduction(Springer Berlin Hei- delberg, 1983)

Reference 59

Resolution: verified fuzzy; raw_fallback, observed 2026-08-08T12:26:38.891866Z

Source-reported events for the cited work

No event found in the named queried sources as of 2026-08-20T06:33:59.587034+00:00.

Observation 26407234-6374-4d81-916f-868176ca6e70 · outbound

This paper cites an unresolved cited work.

Data-driven reconstruction of dynamical systems using Takens' Theorem, manifold learning, and universal function approximators Unresolved cited work

Reference 60

Resolution: unresolved; raw_fallback, observed 2026-08-08T12:26:38.882902Z

Source-reported events for the cited work

No event found in the named queried sources as of 2026-08-20T06:33:59.587034+00:00.

Observation b39ab814-f4bb-4476-a784-55e247583deb · outbound

This paper cites Easley and J.

Data-driven reconstruction of dynamical systems using Takens' Theorem, manifold learning, and universal function approximators Easley and J

Reference 61

Resolution: verified fuzzy; raw_fallback, observed 2026-08-08T12:26:38.873927Z

Source-reported events for the cited work

No event found in the named queried sources as of 2026-08-20T06:33:59.587034+00:00.

Observation d204cb07-d2a1-4f16-b58f-77d029aee568 · outbound

This paper cites Junghare, S.

Data-driven reconstruction of dynamical systems using Takens' Theorem, manifold learning, and universal function approximators Junghare, S

Reference 62

Resolution: verified fuzzy; raw_fallback, observed 2026-08-08T12:26:38.865025Z

Source-reported events for the cited work

No event found in the named queried sources as of 2026-08-20T06:33:59.587034+00:00.

Observation 62db382b-f283-4af8-985b-e00e69154f8e · outbound

This paper cites Henzler-Wildman and D.

Data-driven reconstruction of dynamical systems using Takens' Theorem, manifold learning, and universal function approximators Henzler-Wildman and D

Reference 63

Resolution: unresolved; no resolver link, observed 2026-08-08T12:26:38.515693Z

Source-reported events for the cited work

Unavailable: canonical work link unavailable.

Observation eaa94d40-9133-4b0c-a57a-d26c4af33819 · outbound

This paper cites Lindorff-Larsen, S.

Data-driven reconstruction of dynamical systems using Takens' Theorem, manifold learning, and universal function approximators Lindorff-Larsen, S

Reference 64

Resolution: unresolved; no resolver link, observed 2026-08-08T12:26:38.518697Z

Source-reported events for the cited work

Unavailable: canonical work link unavailable.

Observation 55a64096-3a38-44fe-9670-517fec4934eb · outbound

This paper cites Humphrey, A.

Data-driven reconstruction of dynamical systems using Takens' Theorem, manifold learning, and universal function approximators Humphrey, A

Reference 65

Resolution: verified fuzzy; raw_fallback, observed 2026-08-08T12:26:38.846500Z

Source-reported events for the cited work

No event found in the named queried sources as of 2026-08-20T06:33:59.587034+00:00.

Observation 56dbb595-0840-4961-aaf3-36a4e133b4f6 · outbound

This paper cites Hotelling, Relations between two sets of variates, Biometrika28, 321 (1936).

Data-driven reconstruction of dynamical systems using Takens' Theorem, manifold learning, and universal function approximators Hotelling, Relations between two sets of variates, Biometrika28, 321 (1936)

Reference 66

Resolution: verified fuzzy; raw_fallback, observed 2026-08-08T12:26:38.838105Z

Source-reported events for the cited work

No event found in the named queried sources as of 2026-08-20T06:33:59.587034+00:00.

Observation 64aa0fa7-256f-42d9-8c83-f0f4deeeff69 · outbound

This paper cites Madhavan, Market microstructure: A survey, Journal of Fi- nancial Markets3, 205 (2000).

Data-driven reconstruction of dynamical systems using Takens' Theorem, manifold learning, and universal function approximators Madhavan, Market microstructure: A survey, Journal of Fi- nancial Markets3, 205 (2000)

Reference 67

Resolution: verified fuzzy; raw_fallback, observed 2026-08-08T12:26:38.829144Z

Source-reported events for the cited work

No event found in the named queried sources as of 2026-08-20T06:33:59.587034+00:00.

Observation b3c282bf-8102-4601-b647-e341310bef2a · outbound

This paper cites Bouchaud, J.

Data-driven reconstruction of dynamical systems using Takens' Theorem, manifold learning, and universal function approximators Bouchaud, J

Reference 68

Resolution: verified fuzzy; raw_fallback, observed 2026-08-08T12:26:38.820063Z

Source-reported events for the cited work

No event found in the named queried sources as of 2026-08-20T06:33:59.587034+00:00.

Observation 01f016a6-5a10-4817-a5c7-a8bea6396be6 · outbound

This paper cites an unresolved cited work.

Data-driven reconstruction of dynamical systems using Takens' Theorem, manifold learning, and universal function approximators Unresolved cited work

Reference 69

Resolution: unresolved; raw_fallback, observed 2026-08-08T12:26:38.811453Z

Source-reported events for the cited work

No event found in the named queried sources as of 2026-08-20T06:33:59.587034+00:00.

Observation 9911a62f-203f-4939-a61f-e79b7c16242a · outbound

This paper cites O’Hara,Market Microstructure Theory(Blackwell Publish- ers, 1995).

Data-driven reconstruction of dynamical systems using Takens' Theorem, manifold learning, and universal function approximators O’Hara,Market Microstructure Theory(Blackwell Publish- ers, 1995)

Reference 70

Resolution: verified fuzzy; raw_fallback, observed 2026-08-08T12:26:38.802790Z

Source-reported events for the cited work

No event found in the named queried sources as of 2026-08-20T06:33:59.587034+00:00.

Observation b9a7ebbd-b54c-43f7-a210-c75194110b78 · outbound

This paper cites Roll, A simple implicit measure of the effective bid–ask spread in an efficient market, The Journal of Finance39, 1127 (1984).

Data-driven reconstruction of dynamical systems using Takens' Theorem, manifold learning, and universal function approximators Roll, A simple implicit measure of the effective bid–ask spread in an efficient market, The Journal of Finance39, 1127 (1984)

Reference 71

Resolution: verified fuzzy; raw_fallback, observed 2026-08-08T12:26:38.793155Z

Source-reported events for the cited work

No event found in the named queried sources as of 2026-08-20T06:33:59.587034+00:00.

Observation d3663c81-ec7f-499f-a7aa-c3e48957d29b · outbound

This paper cites an unresolved cited work.

Data-driven reconstruction of dynamical systems using Takens' Theorem, manifold learning, and universal function approximators Unresolved cited work

Reference 72

Resolution: unresolved; raw_fallback, observed 2026-08-08T12:26:38.783929Z

Source-reported events for the cited work

No event found in the named queried sources as of 2026-08-20T06:33:59.587034+00:00.

Observation e599453c-ea7d-4f78-838f-3cd61d06d7e3 · outbound

This paper cites an unresolved cited work.

Data-driven reconstruction of dynamical systems using Takens' Theorem, manifold learning, and universal function approximators Unresolved cited work

Reference 73

Resolution: unresolved; raw_fallback, observed 2026-08-08T12:26:38.775012Z

Source-reported events for the cited work

No event found in the named queried sources as of 2026-08-20T06:33:59.587034+00:00.

Observation afe21087-ba8c-4458-b350-66720b49dc24 · outbound

This paper cites an unresolved cited work.

Data-driven reconstruction of dynamical systems using Takens' Theorem, manifold learning, and universal function approximators Unresolved cited work

Reference 74

Resolution: unresolved; raw_fallback, observed 2026-08-08T12:26:38.765453Z

Source-reported events for the cited work

No event found in the named queried sources as of 2026-08-20T06:33:59.587034+00:00.

Observation 572bb44f-e5ec-48f3-b819-96f6f7730e9a · outbound

This paper cites an unresolved cited work.

Data-driven reconstruction of dynamical systems using Takens' Theorem, manifold learning, and universal function approximators Unresolved cited work

Reference 75

Resolution: unresolved; raw_fallback, observed 2026-08-08T12:26:38.756507Z

Source-reported events for the cited work

No event found in the named queried sources as of 2026-08-20T06:33:59.587034+00:00.

Observation 2a078423-d065-4101-888a-b86ffacfbf4d · outbound

This paper cites Mandelbrot, The variation of certain speculative prices, The Journal of Business36, 394 (1963).

Data-driven reconstruction of dynamical systems using Takens' Theorem, manifold learning, and universal function approximators Mandelbrot, The variation of certain speculative prices, The Journal of Business36, 394 (1963)

Reference 76

Resolution: unresolved; no resolver link, observed 2026-08-08T12:26:38.552862Z

Source-reported events for the cited work

Unavailable: canonical work link unavailable.

Observation 382ca6f6-bdb5-46a7-93fd-148b4bf70beb · outbound

This paper cites an unresolved cited work.

Data-driven reconstruction of dynamical systems using Takens' Theorem, manifold learning, and universal function approximators Unresolved cited work

Reference 77

Resolution: unresolved; raw_fallback, observed 2026-08-08T12:26:38.741813Z

Source-reported events for the cited work

No event found in the named queried sources as of 2026-08-20T06:33:59.587034+00:00.

Observation a359db29-e235-4670-9891-447c413a3706 · outbound

This paper cites Bollerslev, Generalized autoregressive conditional het- eroskedasticity, Journal of Econometrics31, 307 (1986).

Data-driven reconstruction of dynamical systems using Takens' Theorem, manifold learning, and universal function approximators Bollerslev, Generalized autoregressive conditional het- eroskedasticity, Journal of Econometrics31, 307 (1986)

Reference 78

Resolution: verified fuzzy; raw_fallback, observed 2026-08-08T12:26:38.733497Z

Source-reported events for the cited work

No event found in the named queried sources as of 2026-08-20T06:33:59.587034+00:00.

Observation 8557f5c1-4316-4374-ad46-16702fbf81cd · outbound

This paper cites Bollerslev, A conditionally heteroskedastic time series model for speculative prices and rates of return, The Review of Eco- nomics and Statistics69, 542 (1987).

Data-driven reconstruction of dynamical systems using Takens' Theorem, manifold learning, and universal function approximators Bollerslev, A conditionally heteroskedastic time series model for speculative prices and rates of return, The Review of Eco- nomics and Statistics69, 542 (1987)

Reference 79

Resolution: verified fuzzy; raw_fallback, observed 2026-08-08T12:26:38.725016Z

Source-reported events for the cited work

No event found in the named queried sources as of 2026-08-20T06:33:59.587034+00:00.

Observation 3b3165a1-69fa-4b26-9190-92ed7ad29a03 · outbound

This paper cites an unresolved cited work.

Data-driven reconstruction of dynamical systems using Takens' Theorem, manifold learning, and universal function approximators Unresolved cited work

Reference 80

Resolution: unresolved; raw_fallback, observed 2026-08-08T12:26:38.716405Z

Source-reported events for the cited work

No event found in the named queried sources as of 2026-08-20T06:33:59.587034+00:00.

Observation d61f61bd-a357-4e12-a412-ec680d108a51 · outbound

This paper cites an unresolved cited work.

Data-driven reconstruction of dynamical systems using Takens' Theorem, manifold learning, and universal function approximators Unresolved cited work

Reference 81

Resolution: unresolved; raw_fallback, observed 2026-08-08T12:26:38.706996Z

Source-reported events for the cited work

No event found in the named queried sources as of 2026-08-20T06:33:59.587034+00:00.

Observation 7cf504e8-ba6c-4a09-9c4f-1fc904041019 · outbound

This paper cites an unresolved cited work.

Data-driven reconstruction of dynamical systems using Takens' Theorem, manifold learning, and universal function approximators Unresolved cited work

Reference 82

Resolution: unresolved; raw_fallback, observed 2026-08-08T12:26:38.698167Z

Source-reported events for the cited work

No event found in the named queried sources as of 2026-08-20T06:33:59.587034+00:00.

Observation d9c79903-4fb2-4db1-8ffa-1f4182eee58b · outbound

This paper cites an unresolved cited work.

Data-driven reconstruction of dynamical systems using Takens' Theorem, manifold learning, and universal function approximators Unresolved cited work

Reference 83

Resolution: unresolved; raw_fallback, observed 2026-08-08T12:26:38.689558Z

Source-reported events for the cited work

No event found in the named queried sources as of 2026-08-20T06:33:59.587034+00:00.

Observation 724d77cf-d922-4105-92f0-ddf278be7ba0 · outbound

This paper cites an unresolved cited work.

Data-driven reconstruction of dynamical systems using Takens' Theorem, manifold learning, and universal function approximators Unresolved cited work

Reference 84

Resolution: unresolved; raw_fallback, observed 2026-08-08T12:26:38.680755Z

Source-reported events for the cited work

No event found in the named queried sources as of 2026-08-20T06:33:59.587034+00:00.

Observation 3d9f17d9-271a-4ec8-b79b-d9e111a84692 · outbound

This paper cites an unresolved cited work.

Data-driven reconstruction of dynamical systems using Takens' Theorem, manifold learning, and universal function approximators Unresolved cited work

Reference 85

Resolution: unresolved; raw_fallback, observed 2026-08-08T12:26:38.671566Z

Source-reported events for the cited work

No event found in the named queried sources as of 2026-08-20T06:33:59.587034+00:00.

Observation 41a988ae-bdeb-4c57-ae3c-15e88deb7130 · outbound

This paper cites Weron, Estimating long-range dependence: Finite sample properties and confidence intervals, Physica A: Statistical Me- chanics and its Applications312, 285 (2002).

Data-driven reconstruction of dynamical systems using Takens' Theorem, manifold learning, and universal function approximators Weron, Estimating long-range dependence: Finite sample properties and confidence intervals, Physica A: Statistical Me- chanics and its Applications312, 285 (2002)

Reference 86

Resolution: verified fuzzy; raw_fallback, observed 2026-08-08T12:26:38.662133Z

Source-reported events for the cited work

No event found in the named queried sources as of 2026-08-20T06:33:59.587034+00:00.

Observation 7cdce09c-b80d-4b18-96eb-f7e2aafb70df · outbound

This paper cites Aris, How to get the most out of an equation without really trying, Chemical Engineering Education10, 114 (1976).

Data-driven reconstruction of dynamical systems using Takens' Theorem, manifold learning, and universal function approximators Aris, How to get the most out of an equation without really trying, Chemical Engineering Education10, 114 (1976)

Reference 87

Resolution: verified fuzzy; raw_fallback, observed 2026-08-08T12:26:38.652685Z

Source-reported events for the cited work

No event found in the named queried sources as of 2026-08-20T06:33:59.587034+00:00.

Observation 7b9f6b1e-b677-4309-b3b9-53def54ea6a1 · outbound

This paper cites Ma and A.

Data-driven reconstruction of dynamical systems using Takens' Theorem, manifold learning, and universal function approximators Ma and A

Reference 88

Resolution: verified fuzzy; raw_fallback, observed 2026-08-08T12:26:38.642857Z

Source-reported events for the cited work

No event found in the named queried sources as of 2026-08-20T06:33:59.587034+00:00.

Observation cd22e505-3b86-4eda-8d57-a925fc91ce4a · outbound

This paper cites An Emergent Space for Distributed Data with Hidden Internal Order through Manifold Learning.

Data-driven reconstruction of dynamical systems using Takens' Theorem, manifold learning, and universal function approximators An Emergent Space for Distributed Data with Hidden Internal Order through Manifold Learning

Reference 89

Resolution: verified exact; local_arxiv, observed 2026-08-08T12:26:38.620307Z

Source-reported events for the cited work

No event found in the named queried sources as of 2026-08-20T06:33:59.587034+00:00.

Coverage vector

Reference resolution

Outbound references

Pith citing papers