Paper Citation Record · LEDGER

Invariant Measures for Data-Driven Dynamical System Identification: Analysis and Application

As of 19 August 2026, this Paper Citation Record lists 51 of 51 outbound references and 1 inbound Pith citation observation for arXiv:2502.05204.

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2502.05204 v1

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measured 51 of 51 reference resolution

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Pith citing papers itemized under the disclosed page cap.

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51 of 51 outbound references displayed

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Outbound references

Observation a0fa5299-9c3d-4df3-922f-13f1df2336eb · outbound

This paper cites Parameterized neural ordinary differential equations: Applications to computational physics problems.

Invariant Measures for Data-Driven Dynamical System Identification: Analysis and Application Parameterized neural ordinary differential equations: Applications to computational physics problems

Reference 1

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Observation fa17e5f2-b7a7-4509-803b-c5e815c3b28e · outbound

This paper cites A data-driven approach to model calibration for nonlinear dynamical systems.

Invariant Measures for Data-Driven Dynamical System Identification: Analysis and Application A data-driven approach to model calibration for nonlinear dynamical systems

Reference 2

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Observation a53e18ae-a36e-49d5-9f1e-397f36aef1c8 · outbound

This paper cites Parameter estimation with gravitational waves.

Invariant Measures for Data-Driven Dynamical System Identification: Analysis and Application Parameter estimation with gravitational waves

Reference 3

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Observation 60c548d2-389f-4a70-993d-751e2e312b0a · outbound

This paper cites Machine learning in weather prediction and climate analyses—applications and perspectives.

Invariant Measures for Data-Driven Dynamical System Identification: Analysis and Application Machine learning in weather prediction and climate analyses—applications and perspectives

Reference 4

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This paper cites Data-driven modeling and learning in science and engineering.

Invariant Measures for Data-Driven Dynamical System Identification: Analysis and Application Data-driven modeling and learning in science and engineering

Reference 5

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Observation 70db1dda-4c76-49ca-b9fc-8d024cf7ae78 · outbound

This paper cites Fitting ordinary differential equations to chaotic data.

Invariant Measures for Data-Driven Dynamical System Identification: Analysis and Application Fitting ordinary differential equations to chaotic data

Reference 6

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Observation b5e6cbe4-5f63-4a88-8a6e-ea49e1a352ba · outbound

This paper cites Incremental single shooting—a robust method for the estimation of parameters in dynamical systems.

Invariant Measures for Data-Driven Dynamical System Identification: Analysis and Application Incremental single shooting—a robust method for the estimation of parameters in dynamical systems

Reference 7

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Observation 4e68ba65-4d9f-4b7f-ba67-5a8115d46cdf · outbound

This paper cites Discovering governing equations from data by sparse identification of nonlinear dynamical systems.

Invariant Measures for Data-Driven Dynamical System Identification: Analysis and Application Discovering governing equations from data by sparse identification of nonlinear dynamical systems

Reference 8

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Observation 7955c85c-0edf-41ac-a566-594779be17e5 · outbound

This paper cites Stabilized neural ordinary differential equations for long-time forecasting of dynamical systems.

Invariant Measures for Data-Driven Dynamical System Identification: Analysis and Application Stabilized neural ordinary differential equations for long-time forecasting of dynamical systems

Reference 9

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Observation 53d07b2e-d963-4a09-9e21-cb5ec039957c · outbound

This paper cites Parameter estimation in ordinary differential equations for biochemical processes using the method of multiple shooting.

Invariant Measures for Data-Driven Dynamical System Identification: Analysis and Application Parameter estimation in ordinary differential equations for biochemical processes using the method of multiple shooting

Reference 10

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Observation d5134d08-c1be-4c45-ae1d-18db82129dd4 · outbound

This paper cites Optimal transport for parameter identification of chaotic dynamics via invariant measures.

Invariant Measures for Data-Driven Dynamical System Identification: Analysis and Application Optimal transport for parameter identification of chaotic dynamics via invariant measures

Reference 11

Resolution: verified fuzzy; raw_fallback, observed 2026-08-09T19:51:57.355509Z

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Observation 30a729d6-13f6-4613-9e2f-972e483a72b1 · outbound

This paper cites Training neural operators to preserve invariant measures of chaotic attractors.

Invariant Measures for Data-Driven Dynamical System Identification: Analysis and Application Training neural operators to preserve invariant measures of chaotic attractors

Reference 12

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Observation a90b3895-0028-424b-95a9-b0dc7d46ed33 · outbound

This paper cites Embedology.

Invariant Measures for Data-Driven Dynamical System Identification: Analysis and Application Embedology

Reference 13

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Observation b05352c3-3d34-4e4f-bbc3-dd230b878be5 · outbound

This paper cites The dimension of chaotic attractors.

Invariant Measures for Data-Driven Dynamical System Identification: Analysis and Application The dimension of chaotic attractors

Reference 14

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Observation cfae1dc5-c8a9-4d66-b9e7-dad89359cc6d · outbound

This paper cites Numerical approximation of the frobenius–perron operator using the finite volume method.

Invariant Measures for Data-Driven Dynamical System Identification: Analysis and Application Numerical approximation of the frobenius–perron operator using the finite volume method

Reference 15

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Observation 4c393c71-02c9-49cb-9174-8237a0f79329 · outbound

This paper cites On the numerical approximation of the perron-frobenius and koopman operator, 2016.

Invariant Measures for Data-Driven Dynamical System Identification: Analysis and Application On the numerical approximation of the perron-frobenius and koopman operator, 2016

Reference 16

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Observation 7cface78-b3f5-4ef3-930a-7625b0db9190 · outbound

This paper cites Finite approximation for the frobenius-perron operator.

Invariant Measures for Data-Driven Dynamical System Identification: Analysis and Application Finite approximation for the frobenius-perron operator

Reference 17

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Observation 0eea79d2-2263-461e-9f54-1dedeabea569 · outbound

This paper cites Extensive chaos in the lorenz-96 model.

Invariant Measures for Data-Driven Dynamical System Identification: Analysis and Application Extensive chaos in the lorenz-96 model

Reference 18

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Observation 664c0c99-ade1-46d9-a36e-da80cbc91a5e · outbound

This paper cites Detecting strange attractors in turbulence.

Invariant Measures for Data-Driven Dynamical System Identification: Analysis and Application Detecting strange attractors in turbulence

Reference 19

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Observation 9759b88f-da25-4a18-b696-97482c93cd26 · outbound

This paper cites Chaos, fractals, and noise: stochastic aspects of dynamics, volume 97.

Invariant Measures for Data-Driven Dynamical System Identification: Analysis and Application Chaos, fractals, and noise: stochastic aspects of dynamics, volume 97

Reference 20

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Observation 37b1ef1e-7567-40c1-8d27-cafc401e2690 · outbound

This paper cites On the numerical approximation of the Perron-Frobenius and Koopman operator.

Invariant Measures for Data-Driven Dynamical System Identification: Analysis and Application On the numerical approximation of the Perron-Frobenius and Koopman operator

Reference 21

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Observation a9ca0aa9-ed28-40eb-9e84-4a2ea68af945 · outbound

This paper cites What are SRB measures, and which dynamical systems have them? Journal of statistical physics , 108:733–754, 2002.

Invariant Measures for Data-Driven Dynamical System Identification: Analysis and Application What are SRB measures, and which dynamical systems have them? Journal of statistical physics , 108:733–754, 2002

Reference 22

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Observation 04bfcb7d-4752-44e0-9548-484b311c546a · outbound

This paper cites The Lorenz attractor is mixing.

Invariant Measures for Data-Driven Dynamical System Identification: Analysis and Application The Lorenz attractor is mixing

Reference 23

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Observation 87dfad46-fe2a-4f88-b893-e62010454ee7 · outbound

This paper cites Flow prediction using dynamic mode decomposition with time-delay embedding based on local measurement.Physics of Fluids , 33(9), 2021.

Invariant Measures for Data-Driven Dynamical System Identification: Analysis and Application Flow prediction using dynamic mode decomposition with time-delay embedding based on local measurement.Physics of Fluids , 33(9), 2021

Reference 24

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Observation 5cd7999c-c564-4158-8d3e-0c0342ef56d2 · outbound

This paper cites Untangling brain-wide dynamics in consciousness by cross-embedding.

Invariant Measures for Data-Driven Dynamical System Identification: Analysis and Application Untangling brain-wide dynamics in consciousness by cross-embedding

Reference 25

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Observation 097834af-e024-479c-8093-d688f56d6912 · outbound

This paper cites Deep learning delay coordinate dynamics for chaotic attractors from partial observable data.

Invariant Measures for Data-Driven Dynamical System Identification: Analysis and Application Deep learning delay coordinate dynamics for chaotic attractors from partial observable data

Reference 26

Resolution: verified fuzzy; raw_fallback, observed 2026-08-09T19:51:57.226396Z

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This paper cites Practical method for determining the minimum embedding dimension of a scalar time series.

Invariant Measures for Data-Driven Dynamical System Identification: Analysis and Application Practical method for determining the minimum embedding dimension of a scalar time series

Reference 27

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This paper cites almost ev- ery.

Invariant Measures for Data-Driven Dynamical System Identification: Analysis and Application almost ev- ery

Reference 28

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Observation 99cee81c-bece-436f-9d41-918670ea955c · outbound

This paper cites Detecting stochastic governing laws with observa- tion on stationary distributions.

Invariant Measures for Data-Driven Dynamical System Identification: Analysis and Application Detecting stochastic governing laws with observa- tion on stationary distributions

Reference 29

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Observation 88ebc040-b95f-4a28-8a43-a126c9b5ec24 · outbound

This paper cites Comparison of systems with complex behavior.

Invariant Measures for Data-Driven Dynamical System Identification: Analysis and Application Comparison of systems with complex behavior

Reference 30

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This paper cites Parker, Stephan Hoyer, Volodymyr Kuleshov, Fei Sha, and Leonardo Zepeda-N´ u˜ nez.

Invariant Measures for Data-Driven Dynamical System Identification: Analysis and Application Parker, Stephan Hoyer, Volodymyr Kuleshov, Fei Sha, and Leonardo Zepeda-N´ u˜ nez

Reference 31

Resolution: verified fuzzy; raw_fallback, observed 2026-08-09T19:51:57.179157Z

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Observation bc4dddf4-e092-4381-bcaa-5733a1a2fbb3 · outbound

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Invariant Measures for Data-Driven Dynamical System Identification: Analysis and Application Controlling the statistical properties of expanding maps

Reference 32

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This paper cites A simple framework to justify linear response theory.

Invariant Measures for Data-Driven Dynamical System Identification: Analysis and Application A simple framework to justify linear response theory

Reference 33

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Observation d37a53ec-343c-4577-b52e-919c300e94f7 · outbound

This paper cites When are dynamical sys- tems learned from time series data statistically accurate? In The Thirty-eighth Annual Con- ference on Neural Information Processing Systems , 2024.

Invariant Measures for Data-Driven Dynamical System Identification: Analysis and Application When are dynamical sys- tems learned from time series data statistically accurate? In The Thirty-eighth Annual Con- ference on Neural Information Processing Systems , 2024

Reference 34

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Observation 7faababc-d13a-4c6e-8b83-f16c6ba9ce74 · outbound

This paper cites Steady states of Fokker–Planck equations: I.

Invariant Measures for Data-Driven Dynamical System Identification: Analysis and Application Steady states of Fokker–Planck equations: I

Reference 35

Resolution: verified fuzzy; raw_fallback, observed 2026-08-09T19:51:57.137615Z

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Observation a3036eea-575e-4999-9705-103ccb3b0e86 · outbound

This paper cites an unresolved cited work.

Invariant Measures for Data-Driven Dynamical System Identification: Analysis and Application Unresolved cited work

Reference 36

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Observation a515001e-97d7-48a1-bcaf-7b4579b9876b · outbound

This paper cites Efficient grid-based bayesian estimation of nonlinear low-dimensional systems with sparse non-gaussian pdfs.

Invariant Measures for Data-Driven Dynamical System Identification: Analysis and Application Efficient grid-based bayesian estimation of nonlinear low-dimensional systems with sparse non-gaussian pdfs

Reference 37

Resolution: verified fuzzy; raw_fallback, observed 2026-08-09T19:51:57.116137Z

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Observation 5f588cdd-b103-4d40-9893-66f2af2897f8 · outbound

This paper cites Finite volume methods for hyperbolic problems, volume 31.

Invariant Measures for Data-Driven Dynamical System Identification: Analysis and Application Finite volume methods for hyperbolic problems, volume 31

Reference 38

Resolution: verified fuzzy; raw_fallback, observed 2026-08-09T19:51:57.105592Z

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Observation 4f8e34ba-ab86-45f3-95bd-b42c0023e20c · outbound

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Invariant Measures for Data-Driven Dynamical System Identification: Analysis and Application Pagerank beyond the web

Reference 39

Resolution: verified fuzzy; raw_fallback, observed 2026-08-09T19:51:57.095029Z

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Observation 4872df46-65e8-4d3a-a2d8-cabf0cc9d153 · outbound

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Invariant Measures for Data-Driven Dynamical System Identification: Analysis and Application Guckenheimer

Reference 40

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Observation 888eccf8-70c8-484c-9ac4-4eb06ee3c07e · outbound

This paper cites Kingma and Jimmy Ba.

Invariant Measures for Data-Driven Dynamical System Identification: Analysis and Application Kingma and Jimmy Ba

Reference 41

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Observation a7dc5c69-835b-4d35-9006-757a6d8b2c39 · outbound

This paper cites The Lorenz attractor exists.

Invariant Measures for Data-Driven Dynamical System Identification: Analysis and Application The Lorenz attractor exists

Reference 42

Resolution: verified fuzzy; raw_fallback, observed 2026-08-09T19:51:57.067555Z

Source-reported events for the cited work

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Observation e5103c2a-c37b-470f-8572-91928541171d · outbound

This paper cites Spatiotem- poral data fusion and manifold reconstruction in hall thrusters.

Invariant Measures for Data-Driven Dynamical System Identification: Analysis and Application Spatiotem- poral data fusion and manifold reconstruction in hall thrusters

Reference 43

Resolution: verified fuzzy; raw_fallback, observed 2026-08-09T19:51:57.056566Z

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Observation 7dfb3dd4-e7d1-4d17-8127-00597b284791 · outbound

This paper cites Optimal partition choice for invariant measure approximation for one-dimensional maps.

Invariant Measures for Data-Driven Dynamical System Identification: Analysis and Application Optimal partition choice for invariant measure approximation for one-dimensional maps

Reference 44

Resolution: verified fuzzy; raw_fallback, observed 2026-08-09T19:51:57.045375Z

Source-reported events for the cited work

No event found in the named queried sources as of 2026-08-19T06:32:44.657259+00:00.

Observation e6ab1fb5-47d7-4485-b422-90c46c784928 · outbound

This paper cites Constrained k-means clustering.

Invariant Measures for Data-Driven Dynamical System Identification: Analysis and Application Constrained k-means clustering

Reference 45

Resolution: verified fuzzy; raw_fallback, observed 2026-08-09T19:51:57.033466Z

Source-reported events for the cited work

No event found in the named queried sources as of 2026-08-19T06:32:44.657259+00:00.

Observation 928b2260-7bae-4ecc-8551-e1f473bf385d · outbound

This paper cites Caflisch.

Invariant Measures for Data-Driven Dynamical System Identification: Analysis and Application Caflisch

Reference 46

Resolution: unresolved; no resolver link, observed 2026-08-09T19:51:56.908717Z

Source-reported events for the cited work

Unavailable: canonical work link unavailable.

Observation 51c4d04c-c385-479e-822a-58e60865f99f · outbound

This paper cites Interpolating between Optimal Transport and MMD using Sinkhorn Diver- gences.

Invariant Measures for Data-Driven Dynamical System Identification: Analysis and Application Interpolating between Optimal Transport and MMD using Sinkhorn Diver- gences

Reference 47

Resolution: verified fuzzy; raw_fallback, observed 2026-08-09T19:51:57.015368Z

Source-reported events for the cited work

No event found in the named queried sources as of 2026-08-19T06:32:44.657259+00:00.

Observation 4faa283d-1db2-4d0c-ba8c-87cd87b56f2e · outbound

This paper cites Introduction to metric and topological spaces.

Invariant Measures for Data-Driven Dynamical System Identification: Analysis and Application Introduction to metric and topological spaces

Reference 48

Resolution: verified fuzzy; raw_fallback, observed 2026-08-09T19:51:57.003863Z

Source-reported events for the cited work

No event found in the named queried sources as of 2026-08-19T06:32:44.657259+00:00.

Observation 261697bf-b10e-4930-8f50-c48313baa5f2 · outbound

This paper cites Lebesgue almost everywhere.

Invariant Measures for Data-Driven Dynamical System Identification: Analysis and Application Lebesgue almost everywhere

Reference 49

Resolution: verified fuzzy; raw_fallback, observed 2026-08-09T19:51:56.992706Z

Source-reported events for the cited work

No event found in the named queried sources as of 2026-08-19T06:32:44.657259+00:00.

Observation d13c7d9d-d87b-46b1-8b7d-5bc4f6370f4f · outbound

This paper cites an unresolved cited work.

Invariant Measures for Data-Driven Dynamical System Identification: Analysis and Application Unresolved cited work

Reference 50

Resolution: unresolved; raw_fallback, observed 2026-08-09T19:51:56.982574Z

Source-reported events for the cited work

No event found in the named queried sources as of 2026-08-19T06:32:44.657259+00:00.

Observation 67f2f2c0-cefd-4a46-b7c3-a05f9f8c5f91 · outbound

This paper cites an unresolved cited work.

Invariant Measures for Data-Driven Dynamical System Identification: Analysis and Application Unresolved cited work

Reference 51

Resolution: unresolved; raw_fallback, observed 2026-08-09T19:51:56.972108Z

Source-reported events for the cited work

No event found in the named queried sources as of 2026-08-19T06:32:44.657259+00:00.

Pith citing papers

Observation 60ebd3ae-c1c9-4322-802c-29a12cbf9305 · inbound

Parallel-in-Time Training of Recurrent Neural Networks for Dynamical Systems Reconstruction cites this paper.

Parallel-in-Time Training of Recurrent Neural Networks for Dynamical Systems Reconstruction Invariant Measures for Data-Driven Dynamical System Identification: Analysis and Application

Reference 8

Resolution: verified exact; arxiv_id, observed 2026-05-14T21:12:58.655599Z

Source-reported events for the cited work

No event found in the named queried sources as of 2026-08-19T06:32:44.657259+00:00.