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Paper Citation Record · LEDGER

MoTIF: A Mode-Structured Tensor Framework for Multi-Parametric Approximation, Super-Resolution and Forecasting of Unsteady Systems

As of 10 August 2026, this Paper Citation Record lists 54 of 54 outbound references and 1 inbound Pith citation observation for arXiv:2510.25625.

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

pith.paper-citation-record.v1
2510.25625 v2

Coverage vector

measured 54 of 54 reference resolution

Typed states for the displayed outbound observations.

Source: paper_references, paper_reference_links, observed 2026-05-18T03:07:29.843285Z

measured 55 of 55 standing notices

One-hop event checks from named stored sources.

Source: scholarly_work_events, retraction_status_cache, observed 2026-08-09T06:31:02.800959+00:00

measured 1 of 1 inbound itemization

Pith citing papers itemized under the disclosed page cap.

Source: paper_references, paper_reference_links, observed 2026-08-03T10:03:44.610737Z

measured 0 of 1 external citation measurements

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

Source: cited_works

Reference resolution

54 of 54 outbound references displayed

  • verified exact25
  • verified fuzzy18
  • unresolved5
  • parse uncertain0
  • malformed identifier4
  • metadata mismatch2

External citation measurements

No source-named external measurement is stored.

Outbound references

Observation 131828e1-c02a-44a3-9d79-54cf7beca15e · outbound

This paper cites The impact of oceanic heat transport on the atmospheric circulation.

MoTIF: A Mode-Structured Tensor Framework for Multi-Parametric Approximation, Super-Resolution and Forecasting of Unsteady Systems The impact of oceanic heat transport on the atmospheric circulation

Reference 1

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arxiv_id, observed 2026-05-18T03:10:49.459044Z

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No event found in the named queried sources as of 2026-08-09T06:31:02.800959+00:00.

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Observation 8cd2d993-148b-4037-a347-3b81d8bebd47 · outbound

This paper cites Numerical simulation of turbulent sediment transport, from bed load to saltation.

MoTIF: A Mode-Structured Tensor Framework for Multi-Parametric Approximation, Super-Resolution and Forecasting of Unsteady Systems Numerical simulation of turbulent sediment transport, from bed load to saltation

Reference 2

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local_arxiv, observed 2026-05-18T03:10:49.464059Z

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No event found in the named queried sources as of 2026-08-09T06:31:02.800959+00:00.

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Observation 6de66e73-4320-4de3-a0b0-1cac9f91a5e2 · outbound

This paper cites Urban fluid mechanics: Air circulation and contaminant dispersion in cities.

MoTIF: A Mode-Structured Tensor Framework for Multi-Parametric Approximation, Super-Resolution and Forecasting of Unsteady Systems Urban fluid mechanics: Air circulation and contaminant dispersion in cities

Reference 3

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No event found in the named queried sources as of 2026-08-09T06:31:02.800959+00:00.

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Observation 42b3866f-9ab7-4a9e-9a7b-002f55aa7520 · outbound

This paper cites Effect of water–air heat transfer on the spread of thermal pollution in rivers.

MoTIF: A Mode-Structured Tensor Framework for Multi-Parametric Approximation, Super-Resolution and Forecasting of Unsteady Systems Effect of water–air heat transfer on the spread of thermal pollution in rivers

Reference 4

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No event found in the named queried sources as of 2026-08-09T06:31:02.800959+00:00.

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Observation eb5610af-586c-4267-9454-e4894d41fdce · outbound

This paper cites contributors,Application of cfd in thermal power plants,https://en.wikipedia.org/wiki/ Application_of_CFD_in_thermal_power_plants, Accessed: 2025-05-22.

MoTIF: A Mode-Structured Tensor Framework for Multi-Parametric Approximation, Super-Resolution and Forecasting of Unsteady Systems contributors,Application of cfd in thermal power plants,https://en.wikipedia.org/wiki/ Application_of_CFD_in_thermal_power_plants, Accessed: 2025-05-22

Reference 5

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No event found in the named queried sources as of 2026-08-09T06:31:02.800959+00:00.

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Observation 2b6e27f6-64e4-4e9b-acc8-7342b42e8bfc · outbound

This paper cites The future of computational fluid dynamics (CFD) simulation in the chemical process industries.

MoTIF: A Mode-Structured Tensor Framework for Multi-Parametric Approximation, Super-Resolution and Forecasting of Unsteady Systems The future of computational fluid dynamics (CFD) simulation in the chemical process industries

Reference 6

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Source-reported events for the cited work

No event found in the named queried sources as of 2026-08-09T06:31:02.800959+00:00.

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Observation e3fb149a-80a1-4f5a-a2e0-5173366f58b5 · outbound

This paper cites Handling simulation failures of a computationally expensive multiobjective optimization problem in pump design.

MoTIF: A Mode-Structured Tensor Framework for Multi-Parametric Approximation, Super-Resolution and Forecasting of Unsteady Systems Handling simulation failures of a computationally expensive multiobjective optimization problem in pump design

Reference 7

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No event found in the named queried sources as of 2026-08-09T06:31:02.800959+00:00.

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Observation 5c046411-8b15-4e72-bd71-51dbfb74dd4a · outbound

This paper cites an unresolved cited work.

MoTIF: A Mode-Structured Tensor Framework for Multi-Parametric Approximation, Super-Resolution and Forecasting of Unsteady Systems Unresolved cited work

Reference 8

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Source-reported events for the cited work

No event found in the named queried sources as of 2026-08-09T06:31:02.800959+00:00.

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Observation 76aa6f4f-cd07-460f-bbe3-5cadb72d3403 · outbound

This paper cites On the experimental, numerical and data-driven methods to study urban flows.

MoTIF: A Mode-Structured Tensor Framework for Multi-Parametric Approximation, Super-Resolution and Forecasting of Unsteady Systems On the experimental, numerical and data-driven methods to study urban flows

Reference 9

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Observation 6de1d3bb-67e3-447c-b9e1-560326b59657 · outbound

This paper cites Parametric dynamic mode decomposition for reduced order modeling.

MoTIF: A Mode-Structured Tensor Framework for Multi-Parametric Approximation, Super-Resolution and Forecasting of Unsteady Systems Parametric dynamic mode decomposition for reduced order modeling

Reference 10

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arxiv_id, observed 2026-05-18T03:10:48.171002Z

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No event found in the named queried sources as of 2026-08-09T06:31:02.800959+00:00.

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Observation 9c5170b3-e00e-4308-9e21-3e77ab9fd5ca · outbound

This paper cites A novel tensor-based modal decomposition method for reduced order modeling and optimal sparse sensor placement.

MoTIF: A Mode-Structured Tensor Framework for Multi-Parametric Approximation, Super-Resolution and Forecasting of Unsteady Systems A novel tensor-based modal decomposition method for reduced order modeling and optimal sparse sensor placement

Reference 11

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No event found in the named queried sources as of 2026-08-09T06:31:02.800959+00:00.

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Observation 2395db20-d9d3-4581-a1fd-0b2e28141bb3 · outbound

This paper cites TURBULENCE AND THE DYNAMICS OF COHERENT STRUCTURES PART I: COHERENT STRUCTURES.

MoTIF: A Mode-Structured Tensor Framework for Multi-Parametric Approximation, Super-Resolution and Forecasting of Unsteady Systems TURBULENCE AND THE DYNAMICS OF COHERENT STRUCTURES PART I: COHERENT STRUCTURES

Reference 12

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No event found in the named queried sources as of 2026-08-09T06:31:02.800959+00:00.

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Observation 2f678510-6a6b-4ef6-b520-b80584bcac14 · outbound

This paper cites Turbulence and the Dynamics of Coherent Structures. II. Symmetries and Trans- formations.

MoTIF: A Mode-Structured Tensor Framework for Multi-Parametric Approximation, Super-Resolution and Forecasting of Unsteady Systems Turbulence and the Dynamics of Coherent Structures. II. Symmetries and Trans- formations

Reference 13

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No event found in the named queried sources as of 2026-08-09T06:31:02.800959+00:00.

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Observation 1f25f780-40fe-4b1a-8e0a-a298d5d01d21 · outbound

This paper cites an unresolved cited work.

MoTIF: A Mode-Structured Tensor Framework for Multi-Parametric Approximation, Super-Resolution and Forecasting of Unsteady Systems Unresolved cited work

Reference 14

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No event found in the named queried sources as of 2026-08-09T06:31:02.800959+00:00.

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Observation 5e1ae4a6-bf0e-4189-a575-8d34cea22b43 · outbound

This paper cites Model order reduction in aerodynamics: Review and appli- cations.

MoTIF: A Mode-Structured Tensor Framework for Multi-Parametric Approximation, Super-Resolution and Forecasting of Unsteady Systems Model order reduction in aerodynamics: Review and appli- cations

Reference 15

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No event found in the named queried sources as of 2026-08-09T06:31:02.800959+00:00.

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Observation e4e7fe3d-8597-4886-997c-cdea52eed993 · outbound

This paper cites A transient reduced order model for battery thermal management based on singular value decomposition.

MoTIF: A Mode-Structured Tensor Framework for Multi-Parametric Approximation, Super-Resolution and Forecasting of Unsteady Systems A transient reduced order model for battery thermal management based on singular value decomposition

Reference 16

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arxiv_id, observed 2026-05-18T03:10:48.102656Z

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No event found in the named queried sources as of 2026-08-09T06:31:02.800959+00:00.

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Observation 36f205cd-b6a9-4a57-91c5-ce3fd6e8510d · outbound

This paper cites The extension of factor analysis to three-dimensional matrices.

MoTIF: A Mode-Structured Tensor Framework for Multi-Parametric Approximation, Super-Resolution and Forecasting of Unsteady Systems The extension of factor analysis to three-dimensional matrices

Reference 18

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No event found in the named queried sources as of 2026-08-09T06:31:02.800959+00:00.

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Observation 5631dd4c-9a88-4a9f-a5c2-2af5d6aa076a · outbound

This paper cites Quaternion higher-order singular value decomposition and its applications in color image processing.

MoTIF: A Mode-Structured Tensor Framework for Multi-Parametric Approximation, Super-Resolution and Forecasting of Unsteady Systems Quaternion higher-order singular value decomposition and its applications in color image processing

Reference 19

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No event found in the named queried sources as of 2026-08-09T06:31:02.800959+00:00.

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Observation 2562f553-c7e5-443a-94fb-9eeab7debbbf · outbound

This paper cites Nonintrusive model order reduction for cross- diffusion systems.

MoTIF: A Mode-Structured Tensor Framework for Multi-Parametric Approximation, Super-Resolution and Forecasting of Unsteady Systems Nonintrusive model order reduction for cross- diffusion systems

Reference 20

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arxiv_id, observed 2026-05-18T03:10:48.177173Z

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No event found in the named queried sources as of 2026-08-09T06:31:02.800959+00:00.

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Observation 1ec906f8-aafc-4cc6-a7d5-be168d25ad64 · outbound

This paper cites A high fidelity cost efficient tensorial method based on combined pod-hosvd reduced order model of flow field.

MoTIF: A Mode-Structured Tensor Framework for Multi-Parametric Approximation, Super-Resolution and Forecasting of Unsteady Systems A high fidelity cost efficient tensorial method based on combined pod-hosvd reduced order model of flow field

Reference 21

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No event found in the named queried sources as of 2026-08-09T06:31:02.800959+00:00.

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Observation 4e08f79f-9700-4b6c-8c50-a38b8da0fbc7 · outbound

This paper cites Application of hosvd to aerodynamics. the problem of shock waves.

MoTIF: A Mode-Structured Tensor Framework for Multi-Parametric Approximation, Super-Resolution and Forecasting of Unsteady Systems Application of hosvd to aerodynamics. the problem of shock waves

Reference 22

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No event found in the named queried sources as of 2026-08-09T06:31:02.800959+00:00.

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Observation c916c4a4-b5ab-4cc4-aa8b-850a148a15d8 · outbound

This paper cites Kawamata, R.

MoTIF: A Mode-Structured Tensor Framework for Multi-Parametric Approximation, Super-Resolution and Forecasting of Unsteady Systems Kawamata, R

Reference 23

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No event found in the named queried sources as of 2026-08-09T06:31:02.800959+00:00.

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Observation f39fe02b-c60c-4070-a4ba-d88d00f5de91 · outbound

This paper cites Data repairing and resolution enhancement using data-driven modal decomposition and deep learning.

MoTIF: A Mode-Structured Tensor Framework for Multi-Parametric Approximation, Super-Resolution and Forecasting of Unsteady Systems Data repairing and resolution enhancement using data-driven modal decomposition and deep learning

Reference 24

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No event found in the named queried sources as of 2026-08-09T06:31:02.800959+00:00.

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Observation 0f07e127-9a62-448c-b377-fda02a0a1959 · outbound

This paper cites Digital twin of a combustion furnace operating in flameless conditions: Reduced-order model development from cfd simulations.

MoTIF: A Mode-Structured Tensor Framework for Multi-Parametric Approximation, Super-Resolution and Forecasting of Unsteady Systems Digital twin of a combustion furnace operating in flameless conditions: Reduced-order model development from cfd simulations

Reference 25

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Source-reported events for the cited work

No event found in the named queried sources as of 2026-08-09T06:31:02.800959+00:00.

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Observation 625ce870-ce56-4368-8749-178ead020521 · outbound

This paper cites Adaptive digital twins of combustion systems using sparse sensing strategies.

MoTIF: A Mode-Structured Tensor Framework for Multi-Parametric Approximation, Super-Resolution and Forecasting of Unsteady Systems Adaptive digital twins of combustion systems using sparse sensing strategies

Reference 26

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doi, observed 2026-05-18T03:10:48.191572Z

Source-reported events for the cited work

No event found in the named queried sources as of 2026-08-09T06:31:02.800959+00:00.

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Observation 95471f14-9dd5-47ed-a252-c2ed5553bffe · outbound

This paper cites A predictive hybrid reduced order model based on proper orthogonal decomposition combined with deep learning architectures.

MoTIF: A Mode-Structured Tensor Framework for Multi-Parametric Approximation, Super-Resolution and Forecasting of Unsteady Systems A predictive hybrid reduced order model based on proper orthogonal decomposition combined with deep learning architectures

Reference 27

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arxiv_id, observed 2026-05-18T03:10:48.131889Z

Source-reported events for the cited work

No event found in the named queried sources as of 2026-08-09T06:31:02.800959+00:00.

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Observation 2d77fb1e-8ae4-4c7b-8f08-174c1cc2ea19 · outbound

This paper cites Pod-lstm model for predicting pressure time series on structures.

MoTIF: A Mode-Structured Tensor Framework for Multi-Parametric Approximation, Super-Resolution and Forecasting of Unsteady Systems Pod-lstm model for predicting pressure time series on structures

Reference 28

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raw_fallback, observed 2026-05-18T03:12:23.840750Z

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No event found in the named queried sources as of 2026-08-09T06:31:02.800959+00:00.

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Observation 9bb3cb8e-eddf-4fe8-9faf-a67015cadba3 · outbound

This paper cites Generation of aerodynamics databases using high- order singular value decomposition.

MoTIF: A Mode-Structured Tensor Framework for Multi-Parametric Approximation, Super-Resolution and Forecasting of Unsteady Systems Generation of aerodynamics databases using high- order singular value decomposition

Reference 29

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Source-reported events for the cited work

No event found in the named queried sources as of 2026-08-09T06:31:02.800959+00:00.

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Observation 30b709fe-7531-4e57-ad17-fe30926f8c27 · outbound

This paper cites HOSVD-SR: A Physics-Based Deep Learning Framework for Super-Resolution in Fluid Dynamics.

MoTIF: A Mode-Structured Tensor Framework for Multi-Parametric Approximation, Super-Resolution and Forecasting of Unsteady Systems HOSVD-SR: A Physics-Based Deep Learning Framework for Super-Resolution in Fluid Dynamics

Reference 30

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arxiv_id, observed 2026-05-18T03:10:49.444663Z

Source-reported events for the cited work

No event found in the named queried sources as of 2026-08-09T06:31:02.800959+00:00.

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Observation 1ead419a-cb40-440a-9668-37f033cacabd · outbound

This paper cites Hybrid machine learning models based on physical patterns to accelerate CFD simulations: a short guide on autoregressive models.

MoTIF: A Mode-Structured Tensor Framework for Multi-Parametric Approximation, Super-Resolution and Forecasting of Unsteady Systems Hybrid machine learning models based on physical patterns to accelerate CFD simulations: a short guide on autoregressive models

Reference 31

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arxiv_id, observed 2026-05-18T03:10:49.439183Z

Source-reported events for the cited work

No event found in the named queried sources as of 2026-08-09T06:31:02.800959+00:00.

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Observation 3b6b4cf4-1e5a-44f9-a0d7-4917233df650 · outbound

This paper cites Importance of input data normalization for the application of neural net- works to complex industrial problems.

MoTIF: A Mode-Structured Tensor Framework for Multi-Parametric Approximation, Super-Resolution and Forecasting of Unsteady Systems Importance of input data normalization for the application of neural net- works to complex industrial problems

Reference 32

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doi, observed 2026-05-18T03:10:48.135833Z

Source-reported events for the cited work

No event found in the named queried sources as of 2026-08-09T06:31:02.800959+00:00.

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Observation 11b71f15-0303-4e80-94cd-8cf79b1cc42d · outbound

This paper cites Investigating the impact of min-max data normalization on the regression perfor- mance of k-nearest neighbor with different similarity measurements.

MoTIF: A Mode-Structured Tensor Framework for Multi-Parametric Approximation, Super-Resolution and Forecasting of Unsteady Systems Investigating the impact of min-max data normalization on the regression perfor- mance of k-nearest neighbor with different similarity measurements

Reference 33

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This paper cites Normalization Techniques in Training DNNs: Methodology, Analysis and Application.

MoTIF: A Mode-Structured Tensor Framework for Multi-Parametric Approximation, Super-Resolution and Forecasting of Unsteady Systems Normalization Techniques in Training DNNs: Methodology, Analysis and Application

Reference 34

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Observation d238e181-1d82-4c05-b75b-27001051483a · outbound

This paper cites Implications of factor analysis of three-way matrices for measurement of change.

MoTIF: A Mode-Structured Tensor Framework for Multi-Parametric Approximation, Super-Resolution and Forecasting of Unsteady Systems Implications of factor analysis of three-way matrices for measurement of change

Reference 35

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Observation c4efd2ce-702d-45f8-9357-dcd59eb8d1ce · outbound

This paper cites Some mathematical notes on three-mode factor analysis.

MoTIF: A Mode-Structured Tensor Framework for Multi-Parametric Approximation, Super-Resolution and Forecasting of Unsteady Systems Some mathematical notes on three-mode factor analysis

Reference 36

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Observation bf7b2185-9902-411f-832a-a847f3dddf57 · outbound

This paper cites On the best rank-1 and rank-(r1 ,r2 ,. . .,rn) approximation of higher-order tensors.

MoTIF: A Mode-Structured Tensor Framework for Multi-Parametric Approximation, Super-Resolution and Forecasting of Unsteady Systems On the best rank-1 and rank-(r1 ,r2 ,. . .,rn) approximation of higher-order tensors

Reference 37

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Observation bb5878a5-097f-4f9c-b457-90efb45d81c4 · outbound

This paper cites A multilinear sin gu- lar value decomposition.

MoTIF: A Mode-Structured Tensor Framework for Multi-Parametric Approximation, Super-Resolution and Forecasting of Unsteady Systems A multilinear sin gu- lar value decomposition

Reference 38

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No event found in the named queried sources as of 2026-08-09T06:31:02.800959+00:00.

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Observation 1053e936-209b-4d1d-9b6c-e5740c57e442 · outbound

This paper cites Compression of aerodynamic databases using high-order singular value decomposition.

MoTIF: A Mode-Structured Tensor Framework for Multi-Parametric Approximation, Super-Resolution and Forecasting of Unsteady Systems Compression of aerodynamic databases using high-order singular value decomposition

Reference 39

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Observation 731f6a17-aad6-4f7f-8533-64ee7c468b9a · outbound

This paper cites Modelflows-app: Data-driven post-processing and reduced order mod- elling tools.

MoTIF: A Mode-Structured Tensor Framework for Multi-Parametric Approximation, Super-Resolution and Forecasting of Unsteady Systems Modelflows-app: Data-driven post-processing and reduced order mod- elling tools

Reference 40

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Observation 77809498-0c08-41b9-b308-8e917865b766 · outbound

This paper cites Modal Analysis of Fluid Flows: An Overview.

MoTIF: A Mode-Structured Tensor Framework for Multi-Parametric Approximation, Super-Resolution and Forecasting of Unsteady Systems Modal Analysis of Fluid Flows: An Overview

Reference 41

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Observation ed5ff94e-37a7-4cae-a588-35282c6e2a9e · outbound

This paper cites Holmes, J.

MoTIF: A Mode-Structured Tensor Framework for Multi-Parametric Approximation, Super-Resolution and Forecasting of Unsteady Systems Holmes, J

Reference 42

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Observation 64ff78cb-85b4-4d8e-9c42-1659f0b33668 · outbound

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Reference 43

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Reference 44

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Observation f5f555c5-9f73-4301-8f9f-29f35202cdfb · outbound

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Reference 45

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Observation e5762be4-8a0f-4646-a05c-6c4dc129ced2 · outbound

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MoTIF: A Mode-Structured Tensor Framework for Multi-Parametric Approximation, Super-Resolution and Forecasting of Unsteady Systems Long short-term memory

Reference 46

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Observation 1782299c-d181-462d-aeff-b7349850b513 · outbound

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MoTIF: A Mode-Structured Tensor Framework for Multi-Parametric Approximation, Super-Resolution and Forecasting of Unsteady Systems A survey on long short-term memory networks for time series prediction

Reference 47

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Observation fd574243-3e39-49c1-a437-467b26e1e2bd · outbound

This paper cites An lstm-enhanced surrogate model to simulate the dynamics of particle-laden fluid systems.

MoTIF: A Mode-Structured Tensor Framework for Multi-Parametric Approximation, Super-Resolution and Forecasting of Unsteady Systems An lstm-enhanced surrogate model to simulate the dynamics of particle-laden fluid systems

Reference 48

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Observation 8c41437d-b837-45ab-89fb-cbc35267d13f · outbound

This paper cites Chollet et al.,Keras,https://github.com/fchollet/keras.

MoTIF: A Mode-Structured Tensor Framework for Multi-Parametric Approximation, Super-Resolution and Forecasting of Unsteady Systems Chollet et al.,Keras,https://github.com/fchollet/keras

Reference 49

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Observation ac289c9f-b918-4343-8712-65cbb41c3979 · outbound

This paper cites Long short-term memory.

MoTIF: A Mode-Structured Tensor Framework for Multi-Parametric Approximation, Super-Resolution and Forecasting of Unsteady Systems Long short-term memory

Reference 50

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Observation 9a389e9e-b1b6-4274-955f-cce67921044e · outbound

This paper cites Adam: A Method for Stochastic Optimization.

MoTIF: A Mode-Structured Tensor Framework for Multi-Parametric Approximation, Super-Resolution and Forecasting of Unsteady Systems Adam: A Method for Stochastic Optimization

Reference 51

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Observation 0af4d699-3ed8-4876-a057-ae3671d7188a · outbound

This paper cites Turbulent flow around a square cylinder at reynolds number 22,000: A dns study.

MoTIF: A Mode-Structured Tensor Framework for Multi-Parametric Approximation, Super-Resolution and Forecasting of Unsteady Systems Turbulent flow around a square cylinder at reynolds number 22,000: A dns study

Reference 52

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Observation db156dbd-1081-44fd-8943-8b57def1609e · outbound

This paper cites Wake interactions in multibody con- figurations with different shape.

MoTIF: A Mode-Structured Tensor Framework for Multi-Parametric Approximation, Super-Resolution and Forecasting of Unsteady Systems Wake interactions in multibody con- figurations with different shape

Reference 53

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Source-reported events for the cited work

No event found in the named queried sources as of 2026-08-09T06:31:02.800959+00:00.

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Observation d251d2b3-5ed3-4e1f-aeac-1afc1df8393e · outbound

This paper cites Study of various mesh strategies for flow over a square cylinder using OpenFOAM®.

MoTIF: A Mode-Structured Tensor Framework for Multi-Parametric Approximation, Super-Resolution and Forecasting of Unsteady Systems Study of various mesh strategies for flow over a square cylinder using OpenFOAM®

Reference 54

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Observation 393ddce9-bd2b-4b4c-8380-77519b8a55a2 · outbound

This paper cites OpenFOAM: Open source CFD in research and industry.

MoTIF: A Mode-Structured Tensor Framework for Multi-Parametric Approximation, Super-Resolution and Forecasting of Unsteady Systems OpenFOAM: Open source CFD in research and industry

Reference 55

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Source-reported events for the cited work

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Pith citing papers

Observation 562e0ea5-1c3f-451f-89fd-14686e1269bf · inbound

GenDA: Generative Data Assimilation on Complex Urban Areas via Classifier-Free Diffusion Guidance cites this paper.

GenDA: Generative Data Assimilation on Complex Urban Areas via Classifier-Free Diffusion Guidance MoTIF: A Mode-Structured Tensor Framework for Multi-Parametric Approximation, Super-Resolution and Forecasting of Unsteady Systems

Reference 2

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Source-reported events for the cited work

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