Typed states for the displayed outbound observations.
Source: paper_references, paper_reference_links, observed 2026-08-14T10:07:34.694488Z
Paper Citation Record · LEDGER
A Koopman-based framework for forecasting the spatiotemporal evolution of chaotic dynamics with nonlinearities modeled as exogenous forcings
As of 17 August 2026, this Paper Citation Record lists 57 of 57 outbound references and 0 inbound Pith citation observations for arXiv:1909.00076.
A citation records a reference. It does not transfer a finding from one paper to another.
Coverage vector
One-hop event checks from named stored sources.
Source: scholarly_work_events, retraction_status_cache, observed 2026-08-17T06:30:58.91139+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
Reference resolution
57 of 57 outbound references displayed
- verified exact2
- verified fuzzy48
- unresolved6
- parse uncertain0
- malformed identifier1
- metadata mismatch0
External citation measurements
No source-named external measurement is stored.
Outbound references
Observation 1464a6d5-9367-4f90-92e1-3f83ff7619c4 · outbound
This paper cites Deterministic nonperiodic flow,.
A Koopman-based framework for forecasting the spatiotemporal evolution of chaotic dynamics with nonlinearities modeled as exogenous forcings Deterministic nonperiodic flow,
Reference 1
- Resolution
- verified fuzzy
- raw_fallback, observed 2026-08-14T10:07:35.608189Z
Source-reported events for the cited work
No event found in the named queried sources as of 2026-08-17T06:30:58.91139+00:00.
Observation 6ef3403e-3e9d-4dce-90e7-41e4cf5d158b · outbound
This paper cites ˆU ∈ Rnq×r, ˆS ∈ Rr×r and ˆV∈ R(N−q)×r, and [X F]T = ˜U˜S˜V∗ with the truncation value selected as p, so that ˜U∈ R(n+n′)q×p, ˜S∈ Rp×p and ˜V∈ R(N−q)×p.
A Koopman-based framework for forecasting the spatiotemporal evolution of chaotic dynamics with nonlinearities modeled as exogenous forcings ˆU ∈ Rnq×r, ˆS ∈ Rr×r and ˆV∈ R(N−q)×r, and [X F]T = ˜U˜S˜V∗ with the truncation value selected as p, so that ˜U∈ R(n+n′)q×p, ˜S∈ Rp×p and ˜V∈ R(N−q)×p
Reference 2
- Resolution
- malformed identifier
- raw_fallback, observed 2026-08-14T10:07:35.624028Z
Source-reported events for the cited work
No event found in the named queried sources as of 2026-08-17T06:30:58.91139+00:00.
Observation 73daed3d-f81b-465e-bbd2-382ba21683e1 · outbound
This paper cites an unresolved cited work.
A Koopman-based framework for forecasting the spatiotemporal evolution of chaotic dynamics with nonlinearities modeled as exogenous forcings Unresolved cited work
Reference 3
- Resolution
- unresolved
- raw_fallback, observed 2026-08-14T10:07:35.593134Z
Source-reported events for the cited work
No event found in the named queried sources as of 2026-08-17T06:30:58.91139+00:00.
Observation 45543326-3893-4c31-8b33-23e079894a85 · outbound
This paper cites Long-term research challenges in wind energy - A research agenda by the European academy of wind energy,.
A Koopman-based framework for forecasting the spatiotemporal evolution of chaotic dynamics with nonlinearities modeled as exogenous forcings Long-term research challenges in wind energy - A research agenda by the European academy of wind energy,
Reference 4
- Resolution
- verified fuzzy
- raw_fallback, observed 2026-08-14T10:07:35.577670Z
Source-reported events for the cited work
No event found in the named queried sources as of 2026-08-17T06:30:58.91139+00:00.
Observation af4cee4d-38ff-4f40-bdba-a59f42ceebdf · outbound
This paper cites Duriez, S.
Reference 5
- Resolution
- verified fuzzy
- raw_fallback, observed 2026-08-14T10:07:35.562464Z
Source-reported events for the cited work
No event found in the named queried sources as of 2026-08-17T06:30:58.91139+00:00.
Observation e402e289-edd5-45a9-bbfe-17a2bb47e5a3 · outbound
This paper cites Challenges in climate science and contemporary applied mathematics,.
A Koopman-based framework for forecasting the spatiotemporal evolution of chaotic dynamics with nonlinearities modeled as exogenous forcings Challenges in climate science and contemporary applied mathematics,
Reference 6
- Resolution
- verified fuzzy
- raw_fallback, observed 2026-08-14T10:07:35.547335Z
Source-reported events for the cited work
No event found in the named queried sources as of 2026-08-17T06:30:58.91139+00:00.
Observation dbaa2b51-63aa-4e80-97e8-70796f0c0a60 · outbound
This paper cites The quiet revolution of numerical weather prediction,.
A Koopman-based framework for forecasting the spatiotemporal evolution of chaotic dynamics with nonlinearities modeled as exogenous forcings The quiet revolution of numerical weather prediction,
Reference 7
- Resolution
- verified fuzzy
- raw_fallback, observed 2026-08-14T10:07:35.532819Z
Source-reported events for the cited work
No event found in the named queried sources as of 2026-08-17T06:30:58.91139+00:00.
Observation 8f222c87-1188-46cd-92a1-de114696892a · outbound
This paper cites Model error, information barriers, state estimation and prediction in complex multiscale systems,.
A Koopman-based framework for forecasting the spatiotemporal evolution of chaotic dynamics with nonlinearities modeled as exogenous forcings Model error, information barriers, state estimation and prediction in complex multiscale systems,
Reference 8
- Resolution
- verified fuzzy
- raw_fallback, observed 2026-08-14T10:07:35.517839Z
Source-reported events for the cited work
No event found in the named queried sources as of 2026-08-17T06:30:58.91139+00:00.
Observation 17409448-ed8d-4361-bb7f-63eef77b25b4 · outbound
This paper cites A variational approach to probing extreme events in turbu- lent dynamical systems,.
A Koopman-based framework for forecasting the spatiotemporal evolution of chaotic dynamics with nonlinearities modeled as exogenous forcings A variational approach to probing extreme events in turbu- lent dynamical systems,
Reference 9
- Resolution
- verified fuzzy
- raw_fallback, observed 2026-08-14T10:07:35.503071Z
Source-reported events for the cited work
No event found in the named queried sources as of 2026-08-17T06:30:58.91139+00:00.
Observation 3fa13e5d-6cf3-4311-8741-8bb17974b0c0 · outbound
This paper cites The interpretation of short climate records, with comments on the North Atlantic and Southern Oscillations,.
A Koopman-based framework for forecasting the spatiotemporal evolution of chaotic dynamics with nonlinearities modeled as exogenous forcings The interpretation of short climate records, with comments on the North Atlantic and Southern Oscillations,
Reference 10
- Resolution
- verified fuzzy
- raw_fallback, observed 2026-08-14T10:07:35.488151Z
Source-reported events for the cited work
No event found in the named queried sources as of 2026-08-17T06:30:58.91139+00:00.
Observation f4865fdb-565e-4333-b890-88341df96b44 · outbound
This paper cites Van den Dool, Empirical methods in short-term climate prediction (Oxford University Press, 2007).
A Koopman-based framework for forecasting the spatiotemporal evolution of chaotic dynamics with nonlinearities modeled as exogenous forcings Van den Dool, Empirical methods in short-term climate prediction (Oxford University Press, 2007)
Reference 11
- Resolution
- verified fuzzy
- raw_fallback, observed 2026-08-14T10:07:35.472068Z
Source-reported events for the cited work
No event found in the named queried sources as of 2026-08-17T06:30:58.91139+00:00.
Observation de698f90-7fdb-4413-a05e-2ce6a3c37e58 · outbound
This paper cites The skill of atmospheric linear inverse models in hindcasting the Madden–Julian Oscillation,.
A Koopman-based framework for forecasting the spatiotemporal evolution of chaotic dynamics with nonlinearities modeled as exogenous forcings The skill of atmospheric linear inverse models in hindcasting the Madden–Julian Oscillation,
Reference 12
- Resolution
- verified fuzzy
- raw_fallback, observed 2026-08-14T10:07:35.456165Z
Source-reported events for the cited work
No event found in the named queried sources as of 2026-08-17T06:30:58.91139+00:00.
Observation be76a75e-88ae-4f8f-8808-bcb69fc36738 · outbound
This paper cites Data-driven spectral decomposition and forecasting of ergodic dynamical sys- tems,.
A Koopman-based framework for forecasting the spatiotemporal evolution of chaotic dynamics with nonlinearities modeled as exogenous forcings Data-driven spectral decomposition and forecasting of ergodic dynamical sys- tems,
Reference 13
- Resolution
- verified fuzzy
- raw_fallback, observed 2026-08-14T10:07:35.441225Z
Source-reported events for the cited work
No event found in the named queried sources as of 2026-08-17T06:30:58.91139+00:00.
Observation efab9e51-4136-4aea-82fd-4af6e0d7bd76 · outbound
This paper cites Data-driven prediction strategies for low-frequency patterns of North Pacific climate variability,.
A Koopman-based framework for forecasting the spatiotemporal evolution of chaotic dynamics with nonlinearities modeled as exogenous forcings Data-driven prediction strategies for low-frequency patterns of North Pacific climate variability,
Reference 14
- Resolution
- verified fuzzy
- raw_fallback, observed 2026-08-14T10:07:35.425355Z
Source-reported events for the cited work
No event found in the named queried sources as of 2026-08-17T06:30:58.91139+00:00.
Observation 047f3fca-b1d1-408c-922b-c5d0dda300e3 · outbound
This paper cites Data-driven reduced modelling of turbulent Rayleigh- B´ enard convection using dmd-enhanced fluctuation-dissipation theorem,.
A Koopman-based framework for forecasting the spatiotemporal evolution of chaotic dynamics with nonlinearities modeled as exogenous forcings Data-driven reduced modelling of turbulent Rayleigh- B´ enard convection using dmd-enhanced fluctuation-dissipation theorem,
Reference 15
- Resolution
- verified exact
- doi, observed 2026-08-14T10:07:34.734631Z
Source-reported events for the cited work
No event found in the named queried sources as of 2026-08-17T06:30:58.91139+00:00.
Observation 37e78af1-3d2b-4d41-b575-e1c18a1bf725 · outbound
This paper cites An ensemble quadratic echo state network for non-linear spatio-temporal forecasting,.
A Koopman-based framework for forecasting the spatiotemporal evolution of chaotic dynamics with nonlinearities modeled as exogenous forcings An ensemble quadratic echo state network for non-linear spatio-temporal forecasting,
Reference 16
- Resolution
- verified fuzzy
- raw_fallback, observed 2026-08-14T10:07:35.408746Z
Source-reported events for the cited work
No event found in the named queried sources as of 2026-08-17T06:30:58.91139+00:00.
Observation e36a1d65-2989-4d91-abbf-82c9001083af · outbound
This paper cites Long-term Forecasting using Higher Order Tensor RNNs.
A Koopman-based framework for forecasting the spatiotemporal evolution of chaotic dynamics with nonlinearities modeled as exogenous forcings Long-term Forecasting using Higher Order Tensor RNNs
Reference 17
- Resolution
- unresolved
- no resolver link, observed 2026-08-14T10:07:34.503637Z
Source-reported events for the cited work
Unavailable: canonical work link unavailable.
Observation 50a4ce34-6860-4641-b6cb-dc05f64080d2 · outbound
This paper cites Data-driven fore- casting of high-dimensional chaotic systems with long short-term memory networks,.
A Koopman-based framework for forecasting the spatiotemporal evolution of chaotic dynamics with nonlinearities modeled as exogenous forcings Data-driven fore- casting of high-dimensional chaotic systems with long short-term memory networks,
Reference 18
- Resolution
- verified fuzzy
- raw_fallback, observed 2026-08-14T10:07:35.392395Z
Source-reported events for the cited work
No event found in the named queried sources as of 2026-08-17T06:30:58.91139+00:00.
Observation 46a29765-79bc-41ac-8553-c2319ba33ecb · outbound
This paper cites Model-free prediction of large spatiotem- porally chaotic systems from data: A reservoir computing approach,.
A Koopman-based framework for forecasting the spatiotemporal evolution of chaotic dynamics with nonlinearities modeled as exogenous forcings Model-free prediction of large spatiotem- porally chaotic systems from data: A reservoir computing approach,
Reference 19
- Resolution
- verified fuzzy
- raw_fallback, observed 2026-08-14T10:07:35.377102Z
Source-reported events for the cited work
No event found in the named queried sources as of 2026-08-17T06:30:58.91139+00:00.
Observation 1c5c9fa6-0726-4dec-82d5-d4ed5fa018bc · outbound
This paper cites Physics-informed neural networks: A deep learning framework for learning forward and inverse problems involving nonlinear partial differential equations,.
A Koopman-based framework for forecasting the spatiotemporal evolution of chaotic dynamics with nonlinearities modeled as exogenous forcings Physics-informed neural networks: A deep learning framework for learning forward and inverse problems involving nonlinear partial differential equations,
Reference 20
- Resolution
- verified fuzzy
- raw_fallback, observed 2026-08-14T10:07:35.361392Z
Source-reported events for the cited work
No event found in the named queried sources as of 2026-08-17T06:30:58.91139+00:00.
Observation faa6b0c4-2b88-4a4d-9922-791e2ddfd389 · outbound
This paper cites Compressed Convolutional LSTM: An Efficient Deep Learning framework to Model High Fidelity 3D Turbulence.
A Koopman-based framework for forecasting the spatiotemporal evolution of chaotic dynamics with nonlinearities modeled as exogenous forcings Compressed Convolutional LSTM: An Efficient Deep Learning framework to Model High Fidelity 3D Turbulence
Reference 21
- Resolution
- unresolved
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Source-reported events for the cited work
Unavailable: canonical work link unavailable.
Observation 118e00e6-163b-4371-96c4-5c04fd71075f · outbound
This paper cites Deep state networks with uncertainty quantification for spatio-temporal forecasting,.
A Koopman-based framework for forecasting the spatiotemporal evolution of chaotic dynamics with nonlinearities modeled as exogenous forcings Deep state networks with uncertainty quantification for spatio-temporal forecasting,
Reference 22
- Resolution
- verified fuzzy
- raw_fallback, observed 2026-08-14T10:07:35.346094Z
Source-reported events for the cited work
No event found in the named queried sources as of 2026-08-17T06:30:58.91139+00:00.
Observation a2d4b553-d67b-4683-a39d-4cbe92cbe797 · outbound
This paper cites Data-driven prediction of a multi-scale Lorenz 96 chaotic system using deep learning methods: Reservoir computing, ANN, and RNN-LSTM.
A Koopman-based framework for forecasting the spatiotemporal evolution of chaotic dynamics with nonlinearities modeled as exogenous forcings Data-driven prediction of a multi-scale Lorenz 96 chaotic system using deep learning methods: Reservoir computing, ANN, and RNN-LSTM
Reference 23
- Resolution
- unresolved
- no resolver link, observed 2026-08-14T10:07:34.531821Z
Source-reported events for the cited work
Unavailable: canonical work link unavailable.
Observation 3fa49968-40dd-435e-a11d-b75a33d15030 · outbound
This paper cites Hamiltonian systems and transformation in Hilbert space,.
A Koopman-based framework for forecasting the spatiotemporal evolution of chaotic dynamics with nonlinearities modeled as exogenous forcings Hamiltonian systems and transformation in Hilbert space,
Reference 24
- Resolution
- verified fuzzy
- raw_fallback, observed 2026-08-14T10:07:35.330331Z
Source-reported events for the cited work
No event found in the named queried sources as of 2026-08-17T06:30:58.91139+00:00.
Observation a7d988db-e6a7-42cd-8b3b-a4cbba7a0ee5 · outbound
This paper cites Spectral properties of dynamical systems, model reduction and decompositions,.
A Koopman-based framework for forecasting the spatiotemporal evolution of chaotic dynamics with nonlinearities modeled as exogenous forcings Spectral properties of dynamical systems, model reduction and decompositions,
Reference 25
- Resolution
- verified fuzzy
- raw_fallback, observed 2026-08-14T10:07:35.314874Z
Source-reported events for the cited work
No event found in the named queried sources as of 2026-08-17T06:30:58.91139+00:00.
Observation 2b7b8170-b2b0-40a4-b4e1-03985f7197ac · outbound
This paper cites Analysis of fluid flows via spectral properties of the Koopman operator,.
A Koopman-based framework for forecasting the spatiotemporal evolution of chaotic dynamics with nonlinearities modeled as exogenous forcings Analysis of fluid flows via spectral properties of the Koopman operator,
Reference 26
- Resolution
- verified fuzzy
- raw_fallback, observed 2026-08-14T10:07:35.299067Z
Source-reported events for the cited work
No event found in the named queried sources as of 2026-08-17T06:30:58.91139+00:00.
Observation 642faa99-16de-495f-ba2d-d13e2aeae254 · outbound
This paper cites Dynamic mode decomposition of numerical and experimental data,.
A Koopman-based framework for forecasting the spatiotemporal evolution of chaotic dynamics with nonlinearities modeled as exogenous forcings Dynamic mode decomposition of numerical and experimental data,
Reference 27
- Resolution
- verified fuzzy
- raw_fallback, observed 2026-08-14T10:07:35.282903Z
Source-reported events for the cited work
No event found in the named queried sources as of 2026-08-17T06:30:58.91139+00:00.
Observation 9496c846-5945-4678-99fe-6fa695f0b71d · outbound
This paper cites Spectral analysis of nonlinear flows,.
A Koopman-based framework for forecasting the spatiotemporal evolution of chaotic dynamics with nonlinearities modeled as exogenous forcings Spectral analysis of nonlinear flows,
Reference 28
- Resolution
- verified fuzzy
- raw_fallback, observed 2026-08-14T10:07:35.266822Z
Source-reported events for the cited work
No event found in the named queried sources as of 2026-08-17T06:30:58.91139+00:00.
Observation b1b05904-327c-4fda-8f0a-903e36daa6ba · outbound
This paper cites On dynamic mode decomposition: Theory and applications,.
A Koopman-based framework for forecasting the spatiotemporal evolution of chaotic dynamics with nonlinearities modeled as exogenous forcings On dynamic mode decomposition: Theory and applications,
Reference 29
- Resolution
- verified fuzzy
- raw_fallback, observed 2026-08-14T10:07:35.251551Z
Source-reported events for the cited work
No event found in the named queried sources as of 2026-08-17T06:30:58.91139+00:00.
Observation 56e437a4-2cef-4a5b-9df1-c09738811fa4 · outbound
This paper cites A data–driven approximation of the Koopman operator: Extending dynamic mode decomposition,.
A Koopman-based framework for forecasting the spatiotemporal evolution of chaotic dynamics with nonlinearities modeled as exogenous forcings A data–driven approximation of the Koopman operator: Extending dynamic mode decomposition,
Reference 30
- Resolution
- verified fuzzy
- raw_fallback, observed 2026-08-14T10:07:35.235731Z
Source-reported events for the cited work
No event found in the named queried sources as of 2026-08-17T06:30:58.91139+00:00.
Observation 3bc46067-19ec-425c-8ec3-d50a7f40a36a · outbound
This paper cites Ergodic theory, dynamic mode decomposition, and computation of spectral properties of the Koopman operator,.
A Koopman-based framework for forecasting the spatiotemporal evolution of chaotic dynamics with nonlinearities modeled as exogenous forcings Ergodic theory, dynamic mode decomposition, and computation of spectral properties of the Koopman operator,
Reference 31
- Resolution
- verified fuzzy
- raw_fallback, observed 2026-08-14T10:07:35.218890Z
Source-reported events for the cited work
No event found in the named queried sources as of 2026-08-17T06:30:58.91139+00:00.
Observation eaf0a33a-278e-40a9-a64a-5273aa2e9cf3 · outbound
This paper cites Study of dynamics in post-transient flows using Koopman mode decomposition,.
A Koopman-based framework for forecasting the spatiotemporal evolution of chaotic dynamics with nonlinearities modeled as exogenous forcings Study of dynamics in post-transient flows using Koopman mode decomposition,
Reference 32
- Resolution
- verified fuzzy
- raw_fallback, observed 2026-08-14T10:07:35.203005Z
Source-reported events for the cited work
No event found in the named queried sources as of 2026-08-17T06:30:58.91139+00:00.
Observation f6d0da0a-113d-4848-8d60-f6156247cbb1 · outbound
This paper cites On convergence of extended dynamic mode decomposition to the Koopman operator,.
A Koopman-based framework for forecasting the spatiotemporal evolution of chaotic dynamics with nonlinearities modeled as exogenous forcings On convergence of extended dynamic mode decomposition to the Koopman operator,
Reference 33
- Resolution
- verified fuzzy
- raw_fallback, observed 2026-08-14T10:07:35.187758Z
Source-reported events for the cited work
No event found in the named queried sources as of 2026-08-17T06:30:58.91139+00:00.
Observation 302bce05-1bbf-49be-bdc2-32956f356774 · outbound
This paper cites Model reduction for flow analysis and control,.
A Koopman-based framework for forecasting the spatiotemporal evolution of chaotic dynamics with nonlinearities modeled as exogenous forcings Model reduction for flow analysis and control,
Reference 34
- Resolution
- verified fuzzy
- raw_fallback, observed 2026-08-14T10:07:35.171820Z
Source-reported events for the cited work
No event found in the named queried sources as of 2026-08-17T06:30:58.91139+00:00.
Observation 93cd01d4-c435-4f00-b6bc-a1d6ddd79b9b · outbound
This paper cites Detecting strange attractors in turbulence,.
A Koopman-based framework for forecasting the spatiotemporal evolution of chaotic dynamics with nonlinearities modeled as exogenous forcings Detecting strange attractors in turbulence,
Reference 35
- Resolution
- verified fuzzy
- raw_fallback, observed 2026-08-14T10:07:35.156298Z
Source-reported events for the cited work
No event found in the named queried sources as of 2026-08-17T06:30:58.91139+00:00.
Observation 7c5acbb1-34d7-48a0-9e60-4c54d2f457ee · outbound
This paper cites Linear predictors for nonlinear dynamical systems: Koopman oper- ator meets model predictive control,.
A Koopman-based framework for forecasting the spatiotemporal evolution of chaotic dynamics with nonlinearities modeled as exogenous forcings Linear predictors for nonlinear dynamical systems: Koopman oper- ator meets model predictive control,
Reference 36
- Resolution
- verified fuzzy
- raw_fallback, observed 2026-08-14T10:07:35.140034Z
Source-reported events for the cited work
No event found in the named queried sources as of 2026-08-17T06:30:58.91139+00:00.
Observation 06e5b2b4-74aa-42e5-9c67-0e6d490e64b5 · outbound
This paper cites A data-driven Koopman model predictive control framework for nonlinear flows.
A Koopman-based framework for forecasting the spatiotemporal evolution of chaotic dynamics with nonlinearities modeled as exogenous forcings A data-driven Koopman model predictive control framework for nonlinear flows
Reference 37
- Resolution
- unresolved
- no resolver link, observed 2026-08-14T10:07:34.598352Z
Source-reported events for the cited work
Unavailable: canonical work link unavailable.
Observation dada5588-3ec0-45e2-b109-6385cbbf5fb2 · outbound
This paper cites Nonlinear laplacian spectral analysis for time series with intermittency and low-frequency variability,.
A Koopman-based framework for forecasting the spatiotemporal evolution of chaotic dynamics with nonlinearities modeled as exogenous forcings Nonlinear laplacian spectral analysis for time series with intermittency and low-frequency variability,
Reference 38
- Resolution
- verified fuzzy
- raw_fallback, observed 2026-08-14T10:07:35.125160Z
Source-reported events for the cited work
No event found in the named queried sources as of 2026-08-17T06:30:58.91139+00:00.
Observation 11348528-818a-41a6-8116-856efa31bcec · outbound
This paper cites Chaos as an intermittently forced linear system,.
A Koopman-based framework for forecasting the spatiotemporal evolution of chaotic dynamics with nonlinearities modeled as exogenous forcings Chaos as an intermittently forced linear system,
Reference 39
- Resolution
- verified fuzzy
- raw_fallback, observed 2026-08-14T10:07:35.110721Z
Source-reported events for the cited work
No event found in the named queried sources as of 2026-08-17T06:30:58.91139+00:00.
Observation 1970b96a-351f-474c-a2c5-0805dee18080 · outbound
This paper cites Discovering governing equations from data by sparse identification of nonlinear dynamical systems,.
A Koopman-based framework for forecasting the spatiotemporal evolution of chaotic dynamics with nonlinearities modeled as exogenous forcings Discovering governing equations from data by sparse identification of nonlinear dynamical systems,
Reference 40
- Resolution
- verified fuzzy
- raw_fallback, observed 2026-08-14T10:07:35.095136Z
Source-reported events for the cited work
No event found in the named queried sources as of 2026-08-17T06:30:58.91139+00:00.
Observation 9f72c4b6-7785-4927-a3ed-593c8c1d0adc · outbound
This paper cites Matrix pencils in time and frequency domain system identification,.
A Koopman-based framework for forecasting the spatiotemporal evolution of chaotic dynamics with nonlinearities modeled as exogenous forcings Matrix pencils in time and frequency domain system identification,
Reference 41
- Resolution
- verified fuzzy
- raw_fallback, observed 2026-08-14T10:07:35.079844Z
Source-reported events for the cited work
No event found in the named queried sources as of 2026-08-17T06:30:58.91139+00:00.
Observation 4605c522-75b9-45f8-a5c9-7530e1fb9ec6 · outbound
This paper cites Model reduction of bilinear systems in the Loewner framework,.
A Koopman-based framework for forecasting the spatiotemporal evolution of chaotic dynamics with nonlinearities modeled as exogenous forcings Model reduction of bilinear systems in the Loewner framework,
Reference 42
- Resolution
- verified fuzzy
- raw_fallback, observed 2026-08-14T10:07:35.064966Z
Source-reported events for the cited work
No event found in the named queried sources as of 2026-08-17T06:30:58.91139+00:00.
Observation b8de00cf-c9c9-4aeb-8509-57ea054d97f8 · outbound
This paper cites On the Loewner framework for model reduction of Burgers’ equation,.
A Koopman-based framework for forecasting the spatiotemporal evolution of chaotic dynamics with nonlinearities modeled as exogenous forcings On the Loewner framework for model reduction of Burgers’ equation,
Reference 43
- Resolution
- verified fuzzy
- raw_fallback, observed 2026-08-14T10:07:35.050012Z
Source-reported events for the cited work
No event found in the named queried sources as of 2026-08-17T06:30:58.91139+00:00.
Observation c14729d0-589c-4194-a336-32dc74743e37 · outbound
This paper cites Gugercin, C.
Reference 44
- Resolution
- verified fuzzy
- raw_fallback, observed 2026-08-14T10:07:35.034720Z
Source-reported events for the cited work
No event found in the named queried sources as of 2026-08-17T06:30:58.91139+00:00.
Observation 815335be-2c43-4fda-9b83-f6151e10578e · outbound
This paper cites Clustering of Series via Dynamic Mode Decomposition and the Matrix Pencil Method.
A Koopman-based framework for forecasting the spatiotemporal evolution of chaotic dynamics with nonlinearities modeled as exogenous forcings Clustering of Series via Dynamic Mode Decomposition and the Matrix Pencil Method
Reference 45
- Resolution
- verified exact
- local_arxiv, observed 2026-08-14T10:07:34.757258Z
Source-reported events for the cited work
No event found in the named queried sources as of 2026-08-17T06:30:58.91139+00:00.
Observation 07f55ee1-9775-4dd0-a87c-955b5091d71f · outbound
This paper cites Dynamic mode decomposition with control,.
A Koopman-based framework for forecasting the spatiotemporal evolution of chaotic dynamics with nonlinearities modeled as exogenous forcings Dynamic mode decomposition with control,
Reference 46
- Resolution
- verified fuzzy
- raw_fallback, observed 2026-08-14T10:07:35.019039Z
Source-reported events for the cited work
No event found in the named queried sources as of 2026-08-17T06:30:58.91139+00:00.
Observation c187e978-d49b-4b21-a725-b8dfe93d145b · outbound
This paper cites Determining Lyapunov exponents from a time series,.
A Koopman-based framework for forecasting the spatiotemporal evolution of chaotic dynamics with nonlinearities modeled as exogenous forcings Determining Lyapunov exponents from a time series,
Reference 47
- Resolution
- verified fuzzy
- raw_fallback, observed 2026-08-14T10:07:35.003310Z
Source-reported events for the cited work
No event found in the named queried sources as of 2026-08-17T06:30:58.91139+00:00.
Observation 2a27c3b0-dac2-403c-8f12-143a86124e1b · outbound
This paper cites Statistically accurate low-order models for uncertainty quantifi- cation in turbulent dynamical systems,.
A Koopman-based framework for forecasting the spatiotemporal evolution of chaotic dynamics with nonlinearities modeled as exogenous forcings Statistically accurate low-order models for uncertainty quantifi- cation in turbulent dynamical systems,
Reference 48
- Resolution
- verified fuzzy
- raw_fallback, observed 2026-08-14T10:07:34.987646Z
Source-reported events for the cited work
No event found in the named queried sources as of 2026-08-17T06:30:58.91139+00:00.
Observation af49bcaa-5e34-4f11-941b-c468b2cd0e75 · outbound
This paper cites Introduction to turbulent dynamical systems for complex systems,.
A Koopman-based framework for forecasting the spatiotemporal evolution of chaotic dynamics with nonlinearities modeled as exogenous forcings Introduction to turbulent dynamical systems for complex systems,
Reference 49
- Resolution
- verified fuzzy
- raw_fallback, observed 2026-08-14T10:07:34.971461Z
Source-reported events for the cited work
No event found in the named queried sources as of 2026-08-17T06:30:58.91139+00:00.
Observation 0a6ad61e-627f-427a-b62c-4ac2062f59c2 · outbound
This paper cites Low-dimensional reduced-order models for statistical response and uncertainty quantification: Two-layer baroclinic turbulence,.
A Koopman-based framework for forecasting the spatiotemporal evolution of chaotic dynamics with nonlinearities modeled as exogenous forcings Low-dimensional reduced-order models for statistical response and uncertainty quantification: Two-layer baroclinic turbulence,
Reference 50
- Resolution
- verified fuzzy
- raw_fallback, observed 2026-08-14T10:07:34.954974Z
Source-reported events for the cited work
No event found in the named queried sources as of 2026-08-17T06:30:58.91139+00:00.
Observation b4aa50c1-0b4e-4bad-9b63-a96d6af4142c · outbound
This paper cites an unresolved cited work.
A Koopman-based framework for forecasting the spatiotemporal evolution of chaotic dynamics with nonlinearities modeled as exogenous forcings Unresolved cited work
Reference 51
- Resolution
- unresolved
- raw_fallback, observed 2026-08-14T10:07:34.937347Z
Source-reported events for the cited work
No event found in the named queried sources as of 2026-08-17T06:30:58.91139+00:00.
Observation 9a06f893-115c-47b1-a945-b2d988ef8192 · outbound
This paper cites Predictability - a problem partly solved,.
A Koopman-based framework for forecasting the spatiotemporal evolution of chaotic dynamics with nonlinearities modeled as exogenous forcings Predictability - a problem partly solved,
Reference 52
- Resolution
- verified fuzzy
- raw_fallback, observed 2026-08-14T10:07:34.920071Z
Source-reported events for the cited work
No event found in the named queried sources as of 2026-08-17T06:30:58.91139+00:00.
Observation 5173e43a-43c1-4799-9e99-f17439fb3a88 · outbound
This paper cites High-Re solutions for incompressible flow using the Navier-Stokes equations and a multigrid method,.
A Koopman-based framework for forecasting the spatiotemporal evolution of chaotic dynamics with nonlinearities modeled as exogenous forcings High-Re solutions for incompressible flow using the Navier-Stokes equations and a multigrid method,
Reference 53
- Resolution
- verified fuzzy
- raw_fallback, observed 2026-08-14T10:07:34.902781Z
Source-reported events for the cited work
No event found in the named queried sources as of 2026-08-17T06:30:58.91139+00:00.
Observation 1ae8c3c2-0b11-490d-b959-ccfb277afafd · outbound
This paper cites Driven cavity flows by efficient numerical techniques,.
A Koopman-based framework for forecasting the spatiotemporal evolution of chaotic dynamics with nonlinearities modeled as exogenous forcings Driven cavity flows by efficient numerical techniques,
Reference 54
- Resolution
- verified fuzzy
- raw_fallback, observed 2026-08-14T10:07:34.884988Z
Source-reported events for the cited work
No event found in the named queried sources as of 2026-08-17T06:30:58.91139+00:00.
Observation 17f3f084-9cf6-4ee1-a456-8b552462e2c4 · outbound
This paper cites A novel fully implicit finite volume method applied to the lid- driven cavity problempart I: High Reynolds number fow calculations,.
A Koopman-based framework for forecasting the spatiotemporal evolution of chaotic dynamics with nonlinearities modeled as exogenous forcings A novel fully implicit finite volume method applied to the lid- driven cavity problempart I: High Reynolds number fow calculations,
Reference 55
- Resolution
- verified fuzzy
- raw_fallback, observed 2026-08-14T10:07:34.870118Z
Source-reported events for the cited work
No event found in the named queried sources as of 2026-08-17T06:30:58.91139+00:00.
Observation 22e9ed92-736e-4f4f-996f-1b9daf2038fa · outbound
This paper cites Linearly recurrent autoencoder networks for learning dynamics,.
A Koopman-based framework for forecasting the spatiotemporal evolution of chaotic dynamics with nonlinearities modeled as exogenous forcings Linearly recurrent autoencoder networks for learning dynamics,
Reference 56
- Resolution
- verified fuzzy
- raw_fallback, observed 2026-08-14T10:07:34.855063Z
Source-reported events for the cited work
No event found in the named queried sources as of 2026-08-17T06:30:58.91139+00:00.
Observation d3c9cc89-c3c1-41e0-9ace-88b94efb7918 · outbound
This paper cites Singular Value Decomposition (svd) and Principal Component Analysis (PCA),.
A Koopman-based framework for forecasting the spatiotemporal evolution of chaotic dynamics with nonlinearities modeled as exogenous forcings Singular Value Decomposition (svd) and Principal Component Analysis (PCA),
Reference 57
- Resolution
- verified fuzzy
- raw_fallback, observed 2026-08-14T10:07:34.839687Z
Source-reported events for the cited work
No event found in the named queried sources as of 2026-08-17T06:30:58.91139+00:00.
Pith citing papers
No inbound Pith citation observations are available.