Paper Citation Record · LEDGER

Machines that Predict Trajectories from Templates

As of 13 August 2026, this Paper Citation Record lists 41 of 41 outbound references and 0 inbound Pith citation observations for arXiv:2607.11551.

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

pith.paper-citation-record.v1
2607.11551 v1

Coverage vector

measured 41 of 41 reference resolution

Typed states for the displayed outbound observations.

Source: paper_references, paper_reference_links, observed 2026-07-14T04:49:03.001111Z

measured 41 of 41 standing notices

One-hop event checks from named stored sources.

Source: scholarly_work_events, retraction_status_cache, observed 2026-08-13T06:32:02.005865+00:00

measured 0 of 0 inbound itemization

Pith citing papers itemized under the disclosed page cap.

Source: paper_references, paper_reference_links

measured 0 of 1 external citation measurements

A source-named dated measurement, never combined with another source.

Source: cited_works

Reference resolution

41 of 41 outbound references displayed

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External citation measurements

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Outbound references

Observation cecd3256-5fe6-45b8-a1d1-cc77172832d0 · outbound

This paper cites Vapnik,The Nature of Statistical Learning Theory.

Machines that Predict Trajectories from Templates Vapnik,The Nature of Statistical Learning Theory

Reference 1

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Observation edeab503-9e88-42a5-bca6-5467d7141958 · outbound

This paper cites A tutorial on support vector machines for pattern recog- nition,.

Machines that Predict Trajectories from Templates A tutorial on support vector machines for pattern recog- nition,

Reference 2

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Observation 4c9ad2f3-6344-4831-b6f9-2b9bfa2ab06a · outbound

This paper cites Instance-based learning algorithms,.

Machines that Predict Trajectories from Templates Instance-based learning algorithms,

Reference 3

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This paper cites A survey of fault diagnosis and fault-tolerant techniques–part i: Fault diagnosis with model-based and signal-based approaches,.

Machines that Predict Trajectories from Templates A survey of fault diagnosis and fault-tolerant techniques–part i: Fault diagnosis with model-based and signal-based approaches,

Reference 4

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Observation 53796e15-2cef-4fa5-8ca2-8921045ba4ad · outbound

This paper cites Linear predictors for nonlinear dynamical sys- tems: Koopman operator meets model predictive control,.

Machines that Predict Trajectories from Templates Linear predictors for nonlinear dynamical sys- tems: Koopman operator meets model predictive control,

Reference 5

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Observation 8c687cc9-7027-4181-909e-106b927a76cb · outbound

This paper cites Data-driven approximation of the Koopman generator: Model reduction, system identification, and control,.

Machines that Predict Trajectories from Templates Data-driven approximation of the Koopman generator: Model reduction, system identification, and control,

Reference 6

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Observation bffe020d-bdc2-4396-832c-57756213625f · outbound

This paper cites Koopman operator theory: fundamentals, control, and applications.

Machines that Predict Trajectories from Templates Koopman operator theory: fundamentals, control, and applications

Reference 7

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Observation d026737a-af59-453b-a06a-482bb942fb13 · outbound

This paper cites Finite-dimensional observation-spaces for non-linear sys- tems,.

Machines that Predict Trajectories from Templates Finite-dimensional observation-spaces for non-linear sys- tems,

Reference 8

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Observation a385c03c-6a12-4223-b729-cf3f30efb440 · outbound

This paper cites Exact Finite Koopman Embedding of Block-Oriented Polynomial Systems.

Machines that Predict Trajectories from Templates Exact Finite Koopman Embedding of Block-Oriented Polynomial Systems

Reference 9

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This paper cites Great: Grassmannian recursive algorithm for tracking & online system identification,.

Machines that Predict Trajectories from Templates Great: Grassmannian recursive algorithm for tracking & online system identification,

Reference 10

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Observation 1aa0ca58-5dbb-4b2a-b6fa-c7ae9e155abf · outbound

This paper cites Data-driven unknown-input ob- servers and state estimation,.

Machines that Predict Trajectories from Templates Data-driven unknown-input ob- servers and state estimation,

Reference 11

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Observation 0a15d193-d36d-417b-ae04-69ab8826b968 · outbound

This paper cites Data-driven input reconstruction and experimental validation,.

Machines that Predict Trajectories from Templates Data-driven input reconstruction and experimental validation,

Reference 12

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Observation 1937a7ff-612e-41dd-821b-55eabab7a820 · outbound

This paper cites Data-driven inverse of linear systems and application to disturbance observers,.

Machines that Predict Trajectories from Templates Data-driven inverse of linear systems and application to disturbance observers,

Reference 13

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This paper cites On the equivalence of model-based and data-driven approaches to the design of unknown-input observers,.

Machines that Predict Trajectories from Templates On the equivalence of model-based and data-driven approaches to the design of unknown-input observers,

Reference 14

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Observation 128d9783-a43b-4379-a1eb-7bd28da60c19 · outbound

This paper cites Data-enabled predictive control: In the shallows of the DeePC,.

Machines that Predict Trajectories from Templates Data-enabled predictive control: In the shallows of the DeePC,

Reference 15

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This paper cites Linear tracking MPC for nonlinear systems–Part ii: The data-driven case,.

Machines that Predict Trajectories from Templates Linear tracking MPC for nonlinear systems–Part ii: The data-driven case,

Reference 16

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Observation faf67e4a-b85a-4faf-916d-d4a9f9811e9f · outbound

This paper cites Learning- based model predictive control: Toward safe learning in control,.

Machines that Predict Trajectories from Templates Learning- based model predictive control: Toward safe learning in control,

Reference 17

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Observation f08d32c3-9258-45ba-a735-08a167b1ece9 · outbound

This paper cites On direct vs indirect data-driven predictive control,.

Machines that Predict Trajectories from Templates On direct vs indirect data-driven predictive control,

Reference 18

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Observation 621e9d7d-4cf6-4b7b-b3eb-16e6866b7bd7 · outbound

This paper cites From system models to class models: An in-context learning paradigm,.

Machines that Predict Trajectories from Templates From system models to class models: An in-context learning paradigm,

Reference 19

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Observation 504869cc-deb7-4db7-a4ae-10e37fbc7af7 · outbound

This paper cites The asymptotic behavior of attention in transformers,.

Machines that Predict Trajectories from Templates The asymptotic behavior of attention in transformers,

Reference 20

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Observation 5d61ce0a-7279-40a3-aac9-5a6a8783bae5 · outbound

This paper cites Multistability of self-attention dynamics in transformers,.

Machines that Predict Trajectories from Templates Multistability of self-attention dynamics in transformers,

Reference 21

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Observation c7b482b3-4f94-4e94-952a-379003884af8 · outbound

This paper cites Generalization error analysis for selective state-space models through the lens of attention,.

Machines that Predict Trajectories from Templates Generalization error analysis for selective state-space models through the lens of attention,

Reference 22

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Observation 8b4fd750-3652-4c4a-9ed9-14d3cddfec8c · outbound

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Machines that Predict Trajectories from Templates Selection mechanisms for sequence modeling using linear state space models,

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Machines that Predict Trajectories from Templates A note on persistency of excitation,

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This paper cites Willems’ fundamental lemma for state-space systems and its extension to multiple datasets,.

Machines that Predict Trajectories from Templates Willems’ fundamental lemma for state-space systems and its extension to multiple datasets,

Reference 25

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Machines that Predict Trajectories from Templates On the design of persistently exciting inputs for data-driven control of linear and nonlinear systems,

Reference 26

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Machines that Predict Trajectories from Templates On controllability and persistency of excitation in data-driven control: Extensions of willems’ fundamental lemma,

Reference 27

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Machines that Predict Trajectories from Templates Willems’ fundamental lemma for nonlinear systems with koopman linear embedding,

Reference 28

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Machines that Predict Trajectories from Templates From product hilbert spaces to the generalized koop- man operator and the nonlinear fundamental lemma,

Reference 29

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Machines that Predict Trajectories from Templates Data-driven simulation and control,

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Machines that Predict Trajectories from Templates Formulas for data-driven control: Stabilization, optimality, and robustness,

Reference 31

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Machines that Predict Trajectories from Templates Learning controllers for nonlinear systems from data,

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Machines that Predict Trajectories from Templates Data-driven control of large- scale networks with formal guarantees: A small-gain-free approach,

Reference 33

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Machines that Predict Trajectories from Templates Bridging direct and indirect data-driven control formulations via regularizations and relaxations,

Reference 34

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Machines that Predict Trajectories from Templates Verhaegen and V

Reference 35

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Machines that Predict Trajectories from Templates Subspace angles between ARMA models,

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Machines that Predict Trajectories from Templates Behavioral uncertainty quantification for data-driven control,

Reference 37

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Machines that Predict Trajectories from Templates Unresolved cited work

Reference 38

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Machines that Predict Trajectories from Templates Unresolved cited work

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Observation 371c3983-b6f6-420c-84bd-0e1241e4e1e5 · outbound

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Machines that Predict Trajectories from Templates Isidori,Nonlinear control systems

Reference 40

Resolution: unresolved; no resolver link, observed 2026-07-14T04:49:03.001111Z

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This paper cites Linear observer synthesis for nonlinear systems linear observer synthesis for nonlinear systems using Koopman operator framework,.

Machines that Predict Trajectories from Templates Linear observer synthesis for nonlinear systems linear observer synthesis for nonlinear systems using Koopman operator framework,

Reference 41

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