Data-Driven Model Order Reduction with pyMOR | Paper Citation Record

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Data-Driven Model Order Reduction with pyMOR Unresolved cited work

Reference 1

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This paper cites Interpolatory Model Reduction of Large-Scale Dynamical Systems.

Data-Driven Model Order Reduction with pyMOR Interpolatory Model Reduction of Large-Scale Dynamical Systems

Reference 2

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Observation c615ba10-9741-405c-b9b1-f15a1ab4091a · outbound

This paper cites Model Reduction of Bilinear Systems in the Loewner Framework.

Data-Driven Model Order Reduction with pyMOR Model Reduction of Bilinear Systems in the Loewner Framework

Reference 3

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Observation b5801d59-3dfa-4c9f-9d26-7e2981ddaf77 · outbound

This paper cites Antoulas, Christopher A.

Data-Driven Model Order Reduction with pyMOR Antoulas, Christopher A

Reference 4

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This paper cites System-theoretic model order reduction with pyMOR.

Data-Driven Model Order Reduction with pyMOR System-theoretic model order reduction with pyMOR

Reference 5

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This paper cites An ‘empirical Interpolation’ Method: Application to Efficient Reduced- Basis Discretization of Partial Differential Equations.

Data-Driven Model Order Reduction with pyMOR An ‘empirical Interpolation’ Method: Application to Efficient Reduced- Basis Discretization of Partial Differential Equations

Reference 6

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Observation d03f81d7-490f-4f9d-b508-f59b6071b413 · outbound

This paper cites Realization-independentH 2-approximation.

Data-Driven Model Order Reduction with pyMOR Realization-independentH 2-approximation

Reference 7

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This paper cites Benner, J.

Data-Driven Model Order Reduction with pyMOR Benner, J

Reference 8

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This paper cites Benner et al., eds.Model Reduction and Approximation: Theory and Algorithms.

Data-Driven Model Order Reduction with pyMOR Benner et al., eds.Model Reduction and Approximation: Theory and Algorithms

Reference 9

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Observation 61614c4b-877b-4bc4-be37-0828b4dc71db · outbound

This paper cites Interpolation-BasedH 2-Model Reduction of Bilinear Control Systems.

Data-Driven Model Order Reduction with pyMOR Interpolation-BasedH 2-Model Reduction of Bilinear Control Systems

Reference 10

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Observation eafa6973-f973-4dfa-91c3-f13df4f2efd3 · outbound

This paper cites Model order reduction based on moment-matching.

Data-Driven Model Order Reduction with pyMOR Model order reduction based on moment-matching

Reference 11

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Observation 683eab1c-df85-4dff-929b-b65d10aea632 · outbound

This paper cites Truncated Gramians for Bilinear Systems and Their Advantages in Model Order Reduction.

Data-Driven Model Order Reduction with pyMOR Truncated Gramians for Bilinear Systems and Their Advantages in Model Order Reduction

Reference 12

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Observation 51533d05-cb52-4689-a6ec-ed07ea12bd30 · outbound

This paper cites Berlin, Boston: De Gruyter, 2020.doi:10.1515/9783110498967.

Data-Driven Model Order Reduction with pyMOR Berlin, Boston: De Gruyter, 2020.doi:10.1515/9783110498967

Reference 13

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Observation cab4f2ec-a00e-42e5-afcb-ab3c188b13de · outbound

This paper cites Berlin, Boston: De Gruyter, 2020.doi:10.1515/9783110499001.

Data-Driven Model Order Reduction with pyMOR Berlin, Boston: De Gruyter, 2020.doi:10.1515/9783110499001

Reference 14

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Observation 215114f2-26a3-47db-853b-494327a6bbb6 · outbound

This paper cites Balancing-related model reduction methods.

Data-Driven Model Order Reduction with pyMOR Balancing-related model reduction methods

Reference 15

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This paper cites Passivity preserving model reduction via spectral factoriza- tion.

Data-Driven Model Order Reduction with pyMOR Passivity preserving model reduction via spectral factoriza- tion

Reference 16

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This paper cites Galerkin v. Least-Squares Petrov–Galerkin Projection in Nonlinear Model Reduction.

Data-Driven Model Order Reduction with pyMOR Galerkin v. Least-Squares Petrov–Galerkin Projection in Nonlinear Model Reduction

Reference 17

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Observation 5fdaa3d3-4519-4f92-91ee-549bc01a8ed6 · outbound

This paper cites Second-order balanced truncation.

Data-Driven Model Order Reduction with pyMOR Second-order balanced truncation

Reference 18

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Observation ca8b84d6-a24c-4e19-ad2d-d2c59690d2d2 · outbound

This paper cites Structure-Preserving Model Reduction for Nonlin- ear Port-Hamiltonian Systems.

Data-Driven Model Order Reduction with pyMOR Structure-Preserving Model Reduction for Nonlin- ear Port-Hamiltonian Systems

Reference 19

Resolution: verified exact; doi, observed 2026-08-04T01:34:06.061153Z

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Observation e1259322-5309-4f06-bb58-2ebab073f612 · outbound

This paper cites Nonlinear Model Reduction via Discrete Empirical Interpolation.

Data-Driven Model Order Reduction with pyMOR Nonlinear Model Reduction via Discrete Empirical Interpolation

Reference 20

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This paper cites 2019.doi:10.11578/dc.20190408.3.

Data-Driven Model Order Reduction with pyMOR 2019.doi:10.11578/dc.20190408.3

Reference 21

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Observation edb85f59-44ae-4b65-ad92-db3a48041470 · outbound

This paper cites Double Greedy Algorithms: Reduced Basis Methods for Transport Dominated Problems.

Data-Driven Model Order Reduction with pyMOR Double Greedy Algorithms: Reduced Basis Methods for Transport Dominated Problems

Reference 22

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Observation c7eeed1d-2bc5-4a0c-8333-032a1cb7cbb6 · outbound

This paper cites EZyRB: Easy Reduced Basis Method.

Data-Driven Model Order Reduction with pyMOR EZyRB: Easy Reduced Basis Method

Reference 23

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Observation 4cb4c2bc-2452-467e-acca-d18d9065857f · outbound

This paper cites Greedy Algorithms for Reduced Bases in Banach Spaces.

Data-Driven Model Order Reduction with pyMOR Greedy Algorithms for Reduced Bases in Banach Spaces

Reference 24

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This paper cites A New Selection Operator for the Discrete Empirical Interpo- lation Method—Improved A Priori Error Bound and Extensions.

Data-Driven Model Order Reduction with pyMOR A New Selection Operator for the Discrete Empirical Interpo- lation Method—Improved A Priori Error Bound and Extensions

Reference 25

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Observation b4ee410c-1e8b-43f1-9699-10fb47d92419 · outbound

This paper cites pyNIROM–A suite of python modules for non-intrusive reduced order modeling of time-dependent problems.

Data-Driven Model Order Reduction with pyMOR pyNIROM–A suite of python modules for non-intrusive reduced order modeling of time-dependent problems

Reference 26

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Observation d095407b-d4b9-4cc6-9e62-068f84262399 · outbound

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Data-Driven Model Order Reduction with pyMOR Unresolved cited work

Reference 27

Resolution: verified exact; doi, observed 2026-08-04T01:34:05.491969Z

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Observation 9531ef7a-1fe5-4df8-974f-931617ae1ba2 · outbound

This paper cites Data-driven model order reduction of quadratic- bilinear systems.

Data-Driven Model Order Reduction with pyMOR Data-driven model order reduction of quadratic- bilinear systems

Reference 28

Resolution: verified exact; doi, observed 2026-08-04T01:34:05.272539Z

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Observation b6cc9541-26a5-4375-9eb6-76d5ae8a5619 · outbound

This paper cites Data-Driven Balancing of Linear Dynamical Systems.

Data-Driven Model Order Reduction with pyMOR Data-Driven Balancing of Linear Dynamical Systems

Reference 29

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Observation aba6e4fc-3d59-4598-9370-ea4ea238763c · outbound

This paper cites H 2 Model Reduction for Large-Scale Linear Dynam- ical Systems.

Data-Driven Model Order Reduction with pyMOR H 2 Model Reduction for Large-Scale Linear Dynam- ical Systems

Reference 30

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Observation bb489b45-9cf6-4fb5-9a4d-49bf6ff9d9a7 · outbound

This paper cites Structure-preserving tangential interpolation for model reduction of port- Hamiltonian systems.

Data-Driven Model Order Reduction with pyMOR Structure-preserving tangential interpolation for model reduction of port- Hamiltonian systems

Reference 31

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Observation 1cb51f52-b5c1-416c-b76c-b4e82d1bd906 · outbound

This paper cites A Reduced Basis Method for Evolution Schemes with Parameter-Dependent Explicit Operators.

Data-Driven Model Order Reduction with pyMOR A Reduced Basis Method for Evolution Schemes with Parameter-Dependent Explicit Operators

Reference 32

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This paper cites Chapter 2: Reduced Basis Methods for Parametrized PDEs–A Tutorial In- troduction for Stationary and Instationary Problems.

Data-Driven Model Order Reduction with pyMOR Chapter 2: Reduced Basis Methods for Parametrized PDEs–A Tutorial In- troduction for Stationary and Instationary Problems

Reference 33

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Observation 76788963-b3eb-4dc3-95d9-645b1022a555 · outbound

This paper cites A New Certified Hierarchical and Adaptive RB-ML-ROM Surrogate Model for Parametrized PDEs.

Data-Driven Model Order Reduction with pyMOR A New Certified Hierarchical and Adaptive RB-ML-ROM Surrogate Model for Parametrized PDEs

Reference 34

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Observation 5e3f9dab-66ca-4094-8df4-5f8006b6b6be · outbound

This paper cites Fast Evaluation of Time-Harmonic Maxwell’s Equations Using the Reduced Basis Method.

Data-Driven Model Order Reduction with pyMOR Fast Evaluation of Time-Harmonic Maxwell’s Equations Using the Reduced Basis Method

Reference 35

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Observation 3a75ce05-cadc-43b2-a3d3-d546ceedb6f4 · outbound

This paper cites Non-intrusive reduced order modeling of nonlinear problems using neural networks.

Data-Driven Model Order Reduction with pyMOR Non-intrusive reduced order modeling of nonlinear problems using neural networks

Reference 36

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Observation bb52556f-9612-4f61-8c5e-a2b0f285c824 · outbound

This paper cites Hesthaven, Gianluigi Rozza, and Benjamin Stamm.Certified Reduced Basis Methods for Parametrized Partial Differential Equations.

Data-Driven Model Order Reduction with pyMOR Hesthaven, Gianluigi Rozza, and Benjamin Stamm.Certified Reduced Basis Methods for Parametrized Partial Differential Equations

Reference 37

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Observation 8cc67c72-aeae-49e1-b6df-f0d80825f93a · outbound

This paper cites Hierarchical Approximate Proper Orthogonal Decomposition.

Data-Driven Model Order Reduction with pyMOR Hierarchical Approximate Proper Orthogonal Decomposition

Reference 38

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Observation d1a16019-a660-4807-82d4-4f192aa0c6a0 · outbound

This paper cites PyDMD: A Python Package for Robust Dynamic Mode Decomposition.

Data-Driven Model Order Reduction with pyMOR PyDMD: A Python Package for Robust Dynamic Mode Decomposition

Reference 39

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Observation 1691ef60-288d-48c5-9af7-37337cb22409 · outbound

This paper cites Data-Driven Parametrized Model Reduction in the Loewner Framework.

Data-Driven Model Order Reduction with pyMOR Data-Driven Parametrized Model Reduction in the Loewner Framework

Reference 40

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This paper cites An eigensystem realization algorithm for modal parameter identification and model reduction.

Data-Driven Model Order Reduction with pyMOR An eigensystem realization algorithm for modal parameter identification and model reduction

Reference 42

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This paper cites PySINDy: A Comprehensive Python Package for Robust Sparse System Identification.

Data-Driven Model Order Reduction with pyMOR PySINDy: A Comprehensive Python Package for Robust Sparse System Identification

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This paper cites Adam: A Method for Stochastic Optimization.

Data-Driven Model Order Reduction with pyMOR Adam: A Method for Stochastic Optimization

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This paper cites Ver- sion ed93797.

Data-Driven Model Order Reduction with pyMOR Ver- sion ed93797

Reference 45

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Observation 2f6cb824-d651-4ef8-8403-78380d825ff6 · outbound

This paper cites Tangential interpolation-based eigensystem realization algorithm for MIMO systems.

Data-Driven Model Order Reduction with pyMOR Tangential interpolation-based eigensystem realization algorithm for MIMO systems

Reference 46

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Observation 420b9b0b-fcf2-4608-933d-cdfdee406439 · outbound

This paper cites A framework for the solution of the generalized realization problem.

Data-Driven Model Order Reduction with pyMOR A framework for the solution of the generalized realization problem

Reference 47

Resolution: unresolved; no resolver link, observed 2026-08-04T01:32:14.662933Z

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Observation f5a4eb12-1942-46de-a5cb-7e6291ebbffb · outbound

This paper cites PySPOD: A Python Package for Spectral Proper Or- thogonal Decomposition (SPOD).

Data-Driven Model Order Reduction with pyMOR PySPOD: A Python Package for Spectral Proper Or- thogonal Decomposition (SPOD)

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Resolution: verified exact; doi, observed 2026-08-04T01:34:04.646916Z

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Observation 02f5441d-5984-4e96-b914-74ac64062e79 · outbound

This paper cites Balancing and model reduction for second-order form linear sys- tems.

Data-Driven Model Order Reduction with pyMOR Balancing and model reduction for second-order form linear sys- tems

Reference 49

Resolution: verified exact; doi, observed 2026-08-04T01:34:04.397573Z

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Observation 7ecf0231-ce35-4428-8e52-9a0fc3ef595a · outbound

This paper cites pyMOR – Generic Algorithms and Interfaces for Model Order Reduction.

Data-Driven Model Order Reduction with pyMOR pyMOR – Generic Algorithms and Interfaces for Model Order Reduction

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Observation 94646210-786f-43b5-b260-0c163a07881f · outbound

This paper cites Efficient Algorithms for Eigensystem Realization Using Randomized SVD.

Data-Driven Model Order Reduction with pyMOR Efficient Algorithms for Eigensystem Realization Using Randomized SVD

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Resolution: unresolved; no resolver link, observed 2026-08-04T01:32:14.679022Z

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Observation 4c17d8a9-1d2d-4961-9bb6-44c6845687d1 · outbound

This paper cites L2-optimal Reduced-order Modeling Using Parameter-separable Forms.

Data-Driven Model Order Reduction with pyMOR L2-optimal Reduced-order Modeling Using Parameter-separable Forms

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Resolution: verified exact; doi, observed 2026-08-04T01:34:04.120411Z

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Observation 5b4beb78-9af9-454e-a629-1e845d18c9a7 · outbound

This paper cites Parametric Model Order Reduction Using pyMOR.

Data-Driven Model Order Reduction with pyMOR Parametric Model Order Reduction Using pyMOR

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Resolution: verified exact; doi, observed 2026-08-04T01:34:03.814932Z

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Observation f64afa63-c85b-4036-be5f-5c35a8e070d7 · outbound

This paper cites Principal component analysis in linear systems: controllability, observability, and model reduction.

Data-Driven Model Order Reduction with pyMOR Principal component analysis in linear systems: controllability, observability, and model reduction

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Observation e1b0e850-3ad8-429d-89f7-214620d6acff · outbound

This paper cites Port-Hamiltonian Dynamic Mode Decomposition.

Data-Driven Model Order Reduction with pyMOR Port-Hamiltonian Dynamic Mode Decomposition

Reference 56

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Observation 1e4a8375-ef0d-447c-8ddf-b83a5b2396e7 · outbound

This paper cites The AAA Algorithm for Rational Ap- proximation.

Data-Driven Model Order Reduction with pyMOR The AAA Algorithm for Rational Ap- proximation

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This paper cites Reduced Basis Methods: Success, Limitations and Future Challenges.

Data-Driven Model Order Reduction with pyMOR Reduced Basis Methods: Success, Limitations and Future Challenges

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Observation 5a4bbe43-d8b9-4035-bf49-36ead0e34184 · outbound

This paper cites A Model Reduction Framework for Efficient Simulation of Li-Ion Batter- ies.

Data-Driven Model Order Reduction with pyMOR A Model Reduction Framework for Efficient Simulation of Li-Ion Batter- ies

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Observation 3e1f8fc0-af75-47c2-95ac-7728b0eb8d0c · outbound

This paper cites PyTorch: an imperative style, high-performance deep learning library.

Data-Driven Model Order Reduction with pyMOR PyTorch: an imperative style, high-performance deep learning library

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Data-Driven Model Order Reduction with pyMOR Scikit-learn: Machine Learning in Python

Reference 62

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Data-Driven Model Order Reduction with pyMOR Unresolved cited work

Reference 63

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Data-Driven Model Order Reduction with pyMOR Unresolved cited work

Reference 64

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Data-Driven Model Order Reduction with pyMOR Balanced truncation model reduction of second-order systems

Reference 66

Resolution: verified exact; doi, observed 2026-08-04T01:34:03.568207Z

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Data-Driven Model Order Reduction with pyMOR Pressio: Enabling projection-based model reduction for large-scale nonlinear dynamical systems

Reference 67

Resolution: verified exact; local_arxiv, observed 2026-08-04T01:34:03.348947Z

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Data-Driven Model Order Reduction with pyMOR The p-AAA Algorithm for Data- Driven Modeling of Parametric Dynamical Systems

Reference 69

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Data-Driven Model Order Reduction with pyMOR Parametric PDEs Worked Out Problems

Reference 70

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Data-Driven Model Order Reduction with pyMOR Kernel Methods for Surrogate Modeling

Reference 71

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Data-Driven Model Order Reduction with pyMOR Benchmark Computations of Laminar Flow Around a Cylinder

Reference 72

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Data-Driven Model Order Reduction with pyMOR Dynamic mode decomposition of numerical and experimental data

Reference 73

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Unavailable: canonical work link unavailable.

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Data-Driven Model Order Reduction with pyMOR Turbulence and the Dynamics of Coherent Structures Part I: Coherent Structures

Reference 74

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This paper cites Non-intrusive reduced order modeling of unsteady flows using artificial neural networks with application to a combustion problem.

Data-Driven Model Order Reduction with pyMOR Non-intrusive reduced order modeling of unsteady flows using artificial neural networks with application to a combustion problem

Reference 76

Resolution: verified exact; doi, observed 2026-08-04T01:34:02.731295Z

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Observation ccd9de4a-1590-4cc4-a2f4-1fa741bc5263 · outbound

This paper cites Analysis of Target Data-Dependent Greedy Kernel Algorithms: Convergence Rates forf-,f·P- andf /P-Greedy.

Data-Driven Model Order Reduction with pyMOR Analysis of Target Data-Dependent Greedy Kernel Algorithms: Convergence Rates forf-,f·P- andf /P-Greedy

Reference 77

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Source-reported events for the cited work

Unavailable: canonical work link unavailable.

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