Paper Citation Record · LEDGER

On the importance of structural identifiability for machine learning with partially observed dynamical systems

As of 14 August 2026, this Paper Citation Record lists 41 of 41 outbound references and 1 inbound Pith citation observation for arXiv:2502.04131.

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

pith.paper-citation-record.v1
2502.04131 v1

Coverage vector

measured 41 of 41 reference resolution

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Source: paper_references, paper_reference_links, observed 2026-08-08T23:29:54.970180Z

measured 42 of 42 standing notices

One-hop event checks from named stored sources.

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measured 1 of 1 inbound itemization

Pith citing papers itemized under the disclosed page cap.

Source: paper_references, paper_reference_links, observed 2026-08-06T19:52:25.828589Z

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

Source: pith, observed 2026-08-06T19:52:25.924217Z

Reference resolution

41 of 41 outbound references displayed

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External citation measurements

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Outbound references

Observation 7f030d39-eefd-40f0-84ab-3d856aba0890 · outbound

This paper cites Learning similarities between irregularly sampled short multivariate time series from ehrs,.

On the importance of structural identifiability for machine learning with partially observed dynamical systems Learning similarities between irregularly sampled short multivariate time series from ehrs,

Reference 1

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Observation db14d5a2-c5af-4624-a183-9847ee8bda2d · outbound

This paper cites Arma modelled time-series classification for structural health monitoring of civil infrastructure,.

On the importance of structural identifiability for machine learning with partially observed dynamical systems Arma modelled time-series classification for structural health monitoring of civil infrastructure,

Reference 2

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Observation 68c8e93c-1efb-4ee1-a008-155602425cd1 · outbound

This paper cites Emotion recognition from speech signals via a probabilistic echo-state network,.

On the importance of structural identifiability for machine learning with partially observed dynamical systems Emotion recognition from speech signals via a probabilistic echo-state network,

Reference 3

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Observation ed52b187-52fe-4df2-8dca-7435705b87c0 · outbound

This paper cites Multi- variate time-series classification using the hidden-unit logistic model,.

On the importance of structural identifiability for machine learning with partially observed dynamical systems Multi- variate time-series classification using the hidden-unit logistic model,

Reference 4

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Observation f42f6e6d-aec3-4969-982c-a07d54829cd1 · outbound

This paper cites Reservoir computing approaches for representation and classification of multivari- ate time series,.

On the importance of structural identifiability for machine learning with partially observed dynamical systems Reservoir computing approaches for representation and classification of multivari- ate time series,

Reference 5

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Observation 6051d986-ae66-4fb7-a496-c4897d90e802 · outbound

This paper cites Multiclass sparse centroids with application to fast time series classification,.

On the importance of structural identifiability for machine learning with partially observed dynamical systems Multiclass sparse centroids with application to fast time series classification,

Reference 6

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Observation 4406c14a-857a-493b-aa78-6d751b58d47b · outbound

This paper cites Classification framework for partially observed dynamical systems,.

On the importance of structural identifiability for machine learning with partially observed dynamical systems Classification framework for partially observed dynamical systems,

Reference 7

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Observation 7ca5b8ea-a8a9-4dcd-acc5-e2a272a88ccd · outbound

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On the importance of structural identifiability for machine learning with partially observed dynamical systems Aleatory and epistemic uncertainty in probability elicita- tion with an example from hazardous waste management,

Reference 8

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This paper cites Aleatory or epistemic? does it matter?,.

On the importance of structural identifiability for machine learning with partially observed dynamical systems Aleatory or epistemic? does it matter?,

Reference 9

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Observation d3867204-a1af-4bdf-a250-f7d857e6e683 · outbound

This paper cites Aleatoric and epistemic uncertainty in machine learning: an introduction to concepts and methods,.

On the importance of structural identifiability for machine learning with partially observed dynamical systems Aleatoric and epistemic uncertainty in machine learning: an introduction to concepts and methods,

Reference 10

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Observation 6ea7b47b-31f2-4ab5-8508-5dba3217b267 · outbound

This paper cites On structural identifiability,.

On the importance of structural identifiability for machine learning with partially observed dynamical systems On structural identifiability,

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Observation 6018e0ee-6a23-464a-a6ec-d453b7b61920 · outbound

This paper cites System identifiability based on the power series ex- pansion of the solution,.

On the importance of structural identifiability for machine learning with partially observed dynamical systems System identifiability based on the power series ex- pansion of the solution,

Reference 12

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Observation aa63b52f-e2d5-41b6-8aac-8ad9f9236ba3 · outbound

This paper cites On the identifiability and distinguishability of nonlinear parametric models,.

On the importance of structural identifiability for machine learning with partially observed dynamical systems On the identifiability and distinguishability of nonlinear parametric models,

Reference 13

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Observation e61ccb9d-d31f-427a-a482-2a5f060fb0c4 · outbound

This paper cites New results for identifiability of nonlinear systems,.

On the importance of structural identifiability for machine learning with partially observed dynamical systems New results for identifiability of nonlinear systems,

Reference 14

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Observation b241566d-ab88-4740-a63e-06c87ce4cdb3 · outbound

This paper cites State isomorphism approach to global identifi- ability of nonlinear systems,.

On the importance of structural identifiability for machine learning with partially observed dynamical systems State isomorphism approach to global identifi- ability of nonlinear systems,

Reference 15

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Observation 7b33bb7f-7bde-4bee-8919-801d67a9caff · outbound

This paper cites Existence and uniqueness of minimal realizations of nonlinear systems,.

On the importance of structural identifiability for machine learning with partially observed dynamical systems Existence and uniqueness of minimal realizations of nonlinear systems,

Reference 16

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Observation 4694447e-574f-4dc9-8ce5-e03b398ecc36 · outbound

This paper cites Nonlinear observability, identifiability, and per- sistent trajectories,.

On the importance of structural identifiability for machine learning with partially observed dynamical systems Nonlinear observability, identifiability, and per- sistent trajectories,

Reference 17

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Observation d4044cce-e3d4-4d4a-a057-ba6b9ce14756 · outbound

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On the importance of structural identifiability for machine learning with partially observed dynamical systems On global identifiability for arbitrary model parametrizations,

Reference 18

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Observation 3f4b8c9a-a08d-4012-8df2-0e33d5043a10 · outbound

This paper cites A priori parameter identi- fiability in models with non-rational functions,.

On the importance of structural identifiability for machine learning with partially observed dynamical systems A priori parameter identi- fiability in models with non-rational functions,

Reference 19

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Observation 2fce606e-6e83-4249-a279-b5b0d3bb8761 · outbound

This paper cites On structural and practical identifiability,.

On the importance of structural identifiability for machine learning with partially observed dynamical systems On structural and practical identifiability,

Reference 20

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Observation b5cf21f4-6f56-4904-ae15-e398f3d9b89a · outbound

This paper cites Autorepar: A method to obtain identifiable and observable reparameterizations of dynamic models with mechanistic insights,.

On the importance of structural identifiability for machine learning with partially observed dynamical systems Autorepar: A method to obtain identifiable and observable reparameterizations of dynamic models with mechanistic insights,

Reference 21

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Observation c49a7a56-613f-459a-83a8-4eaa6fa25d62 · outbound

This paper cites Structural Identifiability of Dynamic Systems Biology Models,.

On the importance of structural identifiability for machine learning with partially observed dynamical systems Structural Identifiability of Dynamic Systems Biology Models,

Reference 22

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Observation 03ed9434-26fe-45b7-9b67-2c77e93e9b9c · outbound

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On the importance of structural identifiability for machine learning with partially observed dynamical systems Finding and breaking lie symmetries: Implications for structural identifiability and observability in biological modelling,

Reference 23

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Observation 18018a37-8769-4809-b434-66ed2ed2633e · outbound

This paper cites Web-based structural identifiability analyzer,.

On the importance of structural identifiability for machine learning with partially observed dynamical systems Web-based structural identifiability analyzer,

Reference 24

Resolution: verified fuzzy; raw_fallback, observed 2026-08-08T23:29:55.289789Z

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Observation f371002a-eb38-4163-9bfd-28799ca0ee64 · outbound

This paper cites On finding and using identifiable parameter combinations in nonlinear dynamic systems biology models and combos: a novel web implementation,.

On the importance of structural identifiability for machine learning with partially observed dynamical systems On finding and using identifiable parameter combinations in nonlinear dynamic systems biology models and combos: a novel web implementation,

Reference 25

Resolution: verified fuzzy; raw_fallback, observed 2026-08-08T23:29:55.274390Z

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Observation 8402e8bd-25a2-4039-ac3c-c527879eb6ed · outbound

This paper cites Differential elimination for dynamical models via projections with applications to structural identifiability,.

On the importance of structural identifiability for machine learning with partially observed dynamical systems Differential elimination for dynamical models via projections with applications to structural identifiability,

Reference 26

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Observation aac14371-8431-4adc-9602-836409f086ce · outbound

This paper cites Benchmarking tools for a priori identifiability analysis,.

On the importance of structural identifiability for machine learning with partially observed dynamical systems Benchmarking tools for a priori identifiability analysis,

Reference 27

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Observation 03c0b88f-fa03-43fd-b807-df407497e614 · outbound

This paper cites A bayesian framework for parameter estimation in dynamical models,.

On the importance of structural identifiability for machine learning with partially observed dynamical systems A bayesian framework for parameter estimation in dynamical models,

Reference 28

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On the importance of structural identifiability for machine learning with partially observed dynamical systems Bayesian parameter esti- mation for dynamical models in systems biology,

Reference 29

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Observation e952bc5c-39ad-47b9-8f18-ab39f4b83320 · outbound

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On the importance of structural identifiability for machine learning with partially observed dynamical systems Unresolved cited work

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Observation cad4f81a-ceca-40f5-a1e7-3b2fb498eaf3 · outbound

This paper cites Usefulness of the two-compartment open model in pharmacokinetics,.

On the importance of structural identifiability for machine learning with partially observed dynamical systems Usefulness of the two-compartment open model in pharmacokinetics,

Reference 31

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On the importance of structural identifiability for machine learning with partially observed dynamical systems Phys- iologically based pharmacokinetic (pbpk) modeling and simulation ap- proaches: A systematic review of published models, applications, and model verification,

Reference 32

Resolution: verified fuzzy; raw_fallback, observed 2026-08-08T23:29:55.160432Z

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Observation b8412768-b324-4779-a46b-a6b361e34070 · outbound

This paper cites Algorithms for the identifiable parameter combinations and parameter bounds of uniden- tifiable catenary compartmental models,.

On the importance of structural identifiability for machine learning with partially observed dynamical systems Algorithms for the identifiable parameter combinations and parameter bounds of uniden- tifiable catenary compartmental models,

Reference 33

Resolution: verified fuzzy; raw_fallback, observed 2026-08-08T23:29:55.142020Z

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Observation 7bfcfaa9-7861-44af-ab03-9ca0ab92c10c · outbound

This paper cites Learning pharmacokinetic models for in vivo glucocorticoid activation,.

On the importance of structural identifiability for machine learning with partially observed dynamical systems Learning pharmacokinetic models for in vivo glucocorticoid activation,

Reference 34

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This paper cites Identifiable reparametrizations of lin- ear compartment models,.

On the importance of structural identifiability for machine learning with partially observed dynamical systems Identifiable reparametrizations of lin- ear compartment models,

Reference 35

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Observation ee14ac7c-bf7a-4e76-bb51-aa764fd93b18 · outbound

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On the importance of structural identifiability for machine learning with partially observed dynamical systems Kinetics of nutrient-limited transport and microbial growth,

Reference 36

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Observation 603d8bd0-2047-46e3-9727-6dccb1c9a178 · outbound

This paper cites On the practical identifiability of microbial growth mod- els incorporating michaelis-menten type nonlinearities,.

On the importance of structural identifiability for machine learning with partially observed dynamical systems On the practical identifiability of microbial growth mod- els incorporating michaelis-menten type nonlinearities,

Reference 37

Resolution: verified fuzzy; raw_fallback, observed 2026-08-08T23:29:55.073847Z

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Observation 504ae8bb-444d-4415-8321-a04f87422271 · outbound

This paper cites Structural identifiability of the pa- rameters of a nonlinear batch reactor model,.

On the importance of structural identifiability for machine learning with partially observed dynamical systems Structural identifiability of the pa- rameters of a nonlinear batch reactor model,

Reference 38

Resolution: verified fuzzy; raw_fallback, observed 2026-08-08T23:29:55.056958Z

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Observation 8d28ecbb-66b1-4330-851f-c388fa636858 · outbound

This paper cites Extensions to a procedure for gener- ating locally identifiable reparameterisations of unidentifiable systems,.

On the importance of structural identifiability for machine learning with partially observed dynamical systems Extensions to a procedure for gener- ating locally identifiable reparameterisations of unidentifiable systems,

Reference 39

Resolution: verified fuzzy; raw_fallback, observed 2026-08-08T23:29:55.040282Z

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Observation 1420005e-a836-42c3-891f-67278c0abcfc · outbound

This paper cites Physics-informed neural networks: A deep learning framework for solving forward and inverse problems involving nonlinear partial differential equations,.

On the importance of structural identifiability for machine learning with partially observed dynamical systems Physics-informed neural networks: A deep learning framework for solving forward and inverse problems involving nonlinear partial differential equations,

Reference 40

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This paper cites Deep learning for time series classification: a review,.

On the importance of structural identifiability for machine learning with partially observed dynamical systems Deep learning for time series classification: a review,

Reference 41

Resolution: verified fuzzy; raw_fallback, observed 2026-08-08T23:29:55.010713Z

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Pith citing papers

Observation dcbf8d98-625f-470a-9f21-58f61a7eda61 · inbound

Structural Identifiability of Compartmental Models: Recent Progress and Future Directions cites this paper.

Structural Identifiability of Compartmental Models: Recent Progress and Future Directions On the importance of structural identifiability for machine learning with partially observed dynamical systems

Reference 41

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