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Paper Citation Record · LEDGER

Data-Driven Model Order Reduction with pyMOR

As of 19 August 2026, this Paper Citation Record lists 71 of 71 outbound references and 0 inbound Pith citation observations for arXiv:2608.00082.

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

pith.paper-citation-record.v1
2608.00082 v1

Coverage vector

measured 71 of 71 reference resolution

Typed states for the displayed outbound observations.

Source: paper_references, paper_reference_links, observed 2026-08-04T01:32:14.760476Z

measured 71 of 71 standing notices

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Source: scholarly_work_events, retraction_status_cache, observed 2026-08-19T06:32:44.657259+00:00

measured 0 of 0 inbound itemization

Pith citing papers itemized under the disclosed page cap.

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measured 0 of 1 external citation measurements

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Reference resolution

71 of 71 outbound references displayed

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External citation measurements

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Outbound references

Observation 93fb77bf-a1c6-4f5a-92b9-9cf524c0a48a · outbound

This paper cites an unresolved cited work.

Data-Driven Model Order Reduction with pyMOR Unresolved cited work

Reference 1

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Observation d944f059-ca65-4f32-a431-3d41c121594f · outbound

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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Observation 44c383a5-bdf7-454a-b099-9be60b4ef842 · outbound

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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Observation 4d60bc48-b54b-4a93-b509-ec2817c43fb7 · outbound

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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Observation 38207a6e-e01f-4054-b15a-a968de87650f · outbound

This paper cites Benner, J.

Data-Driven Model Order Reduction with pyMOR Benner, J

Reference 8

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Observation 308756d0-a5ca-40c3-a8ec-dc450eb6622f · outbound

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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Observation f0c0d330-8df6-4447-8afa-b5d552764988 · outbound

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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Observation 9979e041-b310-49f2-b009-783961b88772 · outbound

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

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No event found in the named queried sources as of 2026-08-19T06:32:44.657259+00:00.

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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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Observation fc7e7384-f775-41ce-9cae-3b9c1b2f46ae · outbound

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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No event found in the named queried sources as of 2026-08-19T06:32:44.657259+00:00.

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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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Observation ffb3e4e3-cdae-42b5-a44b-8a8719058096 · outbound

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

This paper cites an unresolved cited work.

Data-Driven Model Order Reduction with pyMOR Unresolved cited work

Reference 27

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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

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No event found in the named queried sources as of 2026-08-19T06:32:44.657259+00:00.

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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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Observation 89960dc9-471f-4c34-bdcf-613468dbd92b · outbound

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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Unavailable: canonical work link unavailable.

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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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source=pdf_text observed=2026-08-04T01:32:14.635095Z digest=sha256:220ac2ecf1ab0ac3349a87d65b5f83e5a3d349a38d4d3a557f0fe28499168025

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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source=pdf_text observed=2026-08-04T01:32:14.638885Z digest=sha256:af2fb0094e8c37cd359626fad56ef3c59ae0d54354fd431e8557c94dd0493002

Observation 916b9491-309b-45ab-8d03-547a8d07971b · outbound

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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source=pdf_text observed=2026-08-04T01:32:14.643047Z digest=sha256:278c1021da579e402d4bade1e02002de95bdfc0868eef5c5f264e079ae8842d0

Observation 29176387-299f-4c1f-98dd-43e1a8154429 · outbound

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

Reference 43

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no resolver link, observed 2026-08-04T01:32:14.647062Z

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source=pdf_text observed=2026-08-04T01:32:14.647062Z digest=sha256:4632ed6cf89403b90064f864ab2d79fc6650443563dc4ba974dfa7c4cb3f1cc9

Observation e693765b-350b-4958-a89e-faa3d1045909 · outbound

This paper cites Adam: A Method for Stochastic Optimization.

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

Reference 44

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no resolver link, observed 2026-08-04T01:32:14.650817Z

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source=pdf_text observed=2026-08-04T01:32:14.650817Z digest=sha256:5763881cd97277b0c97cbc74c10eed3ba675c6f049cca5e03108522c2eff4279

Observation e7b7f137-73f3-44e6-836c-1d3626e02418 · outbound

This paper cites Ver- sion ed93797.

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

Reference 45

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malformed identifier
doi_truncated, observed 2026-08-04T01:34:04.978147Z

Source-reported events for the cited work

No event found in the named queried sources as of 2026-08-19T06:32:44.657259+00:00.

source=pdf_text observed=2026-08-04T01:32:14.655195Z digest=sha256:f904d151ca8140aaee36c83b1ead361d48c32ad2623506eec3181cc3df2301a9

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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source=pdf_text observed=2026-08-04T01:32:14.658862Z digest=sha256:7c1dfdf6908527b1e21d32b00b74d534a28f36975eb23b1b17435ee110d16a8d

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

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Source-reported events for the cited work

Unavailable: canonical work link unavailable.

source=pdf_text observed=2026-08-04T01:32:14.662933Z digest=sha256:1b38e46a44e5c3308fe3951d71f91d7a8c5da4d633665d7726be2655ac2e9256

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)

Reference 48

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doi, observed 2026-08-04T01:34:04.646916Z

Source-reported events for the cited work

No event found in the named queried sources as of 2026-08-19T06:32:44.657259+00:00.

source=pdf_text observed=2026-08-04T01:32:14.666827Z digest=sha256:d6c60ee01f744d822a92ea8ee69d1863ef37a274ceeb76715b7d99cf7a1f21ef

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

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

Source-reported events for the cited work

No event found in the named queried sources as of 2026-08-19T06:32:44.657259+00:00.

source=pdf_text observed=2026-08-04T01:32:14.670805Z digest=sha256:848da2d9c7d8e6b592e4dd0d29b18541618a1ff6ed6af9e575ea60dc31966314

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

Reference 50

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source=pdf_text observed=2026-08-04T01:32:14.675078Z digest=sha256:140921d91a19875cb0bafcbc3cf0283b914c3c52a4609c8a9b497ae14a38234d

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

Reference 51

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source=pdf_text observed=2026-08-04T01:32:14.679022Z digest=sha256:de06da89def2c6d5eee7c582d8ab36586437b3468820b2db6323c74e57b437d1

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

Reference 52

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

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No event found in the named queried sources as of 2026-08-19T06:32:44.657259+00:00.

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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

Reference 53

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doi, observed 2026-08-04T01:34:03.814932Z

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No event found in the named queried sources as of 2026-08-19T06:32:44.657259+00:00.

source=pdf_text observed=2026-08-04T01:32:14.686541Z digest=sha256:1fcb15a93587fd9e9136bc989478c9e2cc17d7e5a53aabd6643e03c9d091a49b

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

Reference 55

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no resolver link, observed 2026-08-04T01:32:14.690700Z

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source=pdf_text observed=2026-08-04T01:32:14.690700Z digest=sha256:94e7f23c6ce9d1ea9328009b79cbe0122875151f0826e94d91dae8eeb83cdc9b

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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source=pdf_text observed=2026-08-04T01:32:14.693939Z digest=sha256:fe409187afc554386a2207c694ee26880e88f59a717907b30376d29a6ccd83b6

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

Reference 57

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source=pdf_text observed=2026-08-04T01:32:14.697487Z digest=sha256:3a42ecb2e5d5cc408f397ebaa40816d046207bf59cf8e492c32f9ccd252e501c

Observation add18908-24a5-4bb0-bc7e-579252d78319 · outbound

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

Reference 58

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Unavailable: canonical work link unavailable.

source=pdf_text observed=2026-08-04T01:32:14.701362Z digest=sha256:679298a9dc50ea1e4b7d499501d2a3dd4bf9d1235dfa1be2a768b85ebac368d0

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

Reference 59

Resolution
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no resolver link, observed 2026-08-04T01:32:14.705469Z

Source-reported events for the cited work

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source=pdf_text observed=2026-08-04T01:32:14.705469Z digest=sha256:f871f12aca3c172e8a031a47046cf7944268aedb991e6af067f15fc3ca81f927

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

Reference 61

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source=pdf_text observed=2026-08-04T01:32:14.709261Z digest=sha256:bca025017673fd7ef2339a675e7b37c31052b65df657b0d9ece882a2ea46108b

Observation 65bfcf31-ac3d-418d-a275-f6cc3c0f926e · outbound

This paper cites Scikit-learn: Machine Learning in Python.

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

Reference 62

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source=pdf_text observed=2026-08-04T01:32:14.713072Z digest=sha256:a29b9a2565ebfd5ee546c1be4ba036a84ae7e9b5e96b03a824f7ef0bf3869977

Observation dc8b32e4-a519-45b3-9b19-73f2c2cde90c · outbound

This paper cites an unresolved cited work.

Data-Driven Model Order Reduction with pyMOR Unresolved cited work

Reference 63

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source=pdf_text observed=2026-08-04T01:32:14.717317Z digest=sha256:95b99006a2be26671b0a0470521c26473fc70b24e595f3433734e3b426adeb6c

Observation 74d786ed-b5f8-48c5-95d4-5cc1c0e0d665 · outbound

This paper cites an unresolved cited work.

Data-Driven Model Order Reduction with pyMOR Unresolved cited work

Reference 64

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source=pdf_text observed=2026-08-04T01:32:14.721141Z digest=sha256:58bf9422a5b28dd61a7da8105b781ee24732b4d4df85224cdc00adf53ecb9036

Observation 606176cf-6e54-4bf3-8ab6-5eabdc5e1d29 · outbound

This paper cites Balanced truncation model reduction of second-order systems.

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

Reference 66

Resolution
verified exact
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No event found in the named queried sources as of 2026-08-19T06:32:44.657259+00:00.

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Observation 305e67c9-6d08-43d9-94c7-f9195f864175 · outbound

This paper cites Pressio: Enabling projection-based model reduction for large-scale nonlinear dynamical systems.

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

Reference 67

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local_arxiv, observed 2026-08-04T01:34:03.348947Z

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No event found in the named queried sources as of 2026-08-19T06:32:44.657259+00:00.

source=pdf_text observed=2026-08-04T01:32:14.728146Z digest=sha256:d6a668ae04d3168a308d341e6b94f445fec6b095360dd982d8015f2b9ea860f9

Observation 43c018e6-1260-4d72-b110-72551264aad0 · outbound

This paper cites The p-AAA Algorithm for Data- Driven Modeling of Parametric Dynamical Systems.

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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doi, observed 2026-08-04T01:34:02.988694Z

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No event found in the named queried sources as of 2026-08-19T06:32:44.657259+00:00.

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Observation 0317728d-506b-4e15-96a9-8f6cee0dad75 · outbound

This paper cites Parametric PDEs Worked Out Problems.

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

Reference 70

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source=pdf_text observed=2026-08-04T01:32:14.738507Z digest=sha256:307b2a0dd68d2d284e780003e2a30987adcc9f6fd560d857d833cf7a57bb11c2

Observation d5c5b051-d5e2-4595-b466-a37c1a7974ec · outbound

This paper cites Kernel Methods for Surrogate Modeling.

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

Reference 71

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Source-reported events for the cited work

Unavailable: canonical work link unavailable.

source=pdf_text observed=2026-08-04T01:32:14.742655Z digest=sha256:a8f8d4cc831000b8b4749e8f771db7602acd68c79513791cbd50ec977175af25

Observation 5631ed43-78be-4144-a9c1-52905f866a36 · outbound

This paper cites Benchmark Computations of Laminar Flow Around a Cylinder.

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

Reference 72

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no resolver link, observed 2026-08-04T01:32:14.746148Z

Source-reported events for the cited work

Unavailable: canonical work link unavailable.

source=pdf_text observed=2026-08-04T01:32:14.746148Z digest=sha256:ed356e4c1255544e6ec5120cbd419b469ceb24c4b242fead82a7436649578771

Observation 2492bbd4-6d8a-4295-a9c5-dd90e5e45d65 · outbound

This paper cites Dynamic mode decomposition of numerical and experimental data.

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

Reference 73

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unresolved
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Source-reported events for the cited work

Unavailable: canonical work link unavailable.

source=pdf_text observed=2026-08-04T01:32:14.749476Z digest=sha256:5e57934123ed141b0537556501399c8a5fb0297b74c7b52ad55f7716fd966373

Observation 1d8394fc-0160-4ee4-ab47-8ddcb1cb545e · outbound

This paper cites Turbulence and the Dynamics of Coherent Structures Part I: Coherent Structures.

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

Reference 74

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Source-reported events for the cited work

Unavailable: canonical work link unavailable.

source=pdf_text observed=2026-08-04T01:32:14.752773Z digest=sha256:306c0c9acd483684a84e464ff7c59351da4f0d915b274d20675d03ff02e8cce6

Observation fc7a479a-ec32-47bb-99a4-94d52e6a47cd · outbound

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

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doi, observed 2026-08-04T01:34:02.731295Z

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No event found in the named queried sources as of 2026-08-19T06:32:44.657259+00:00.

source=pdf_text observed=2026-08-04T01:32:14.756225Z digest=sha256:8bf504f4d222d5981f8c1d988e1941033771a0e665e88f0a7a4f4b54f804722a

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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unresolved
no resolver link, observed 2026-08-04T01:32:14.760476Z

Source-reported events for the cited work

Unavailable: canonical work link unavailable.

source=pdf_text observed=2026-08-04T01:32:14.760476Z digest=sha256:8bcfb396b73be8ab27607aa1d279f7e379960758e65ac045841d79ae9b358afc

Pith citing papers

No inbound Pith citation observations are available.