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Paper Citation Record · LEDGER

Inverse Design with Dynamic Mode Decomposition

As of 8 August 2026, this Paper Citation Record lists 61 of 61 outbound references and 0 inbound Pith citation observations for arXiv:2502.09490.

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

pith.paper-citation-record.v1
2502.09490 v1

Coverage vector

measured 61 of 61 reference resolution

Typed states for the displayed outbound observations.

Source: paper_references, paper_reference_links, observed 2026-08-07T21:24:02.533408Z

measured 61 of 61 standing notices

One-hop event checks from named stored sources.

Source: scholarly_work_events, retraction_status_cache, observed 2026-08-08T06:32:00.761636+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

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

Source: cited_works

Reference resolution

61 of 61 outbound references displayed

  • verified exact0
  • verified fuzzy43
  • unresolved18
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  • malformed identifier0
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External citation measurements

No source-named external measurement is stored.

Outbound references

Observation c9e09b14-18a3-44cc-b0cb-fb51fb93c89b · outbound

This paper cites Springer, 1966.

Inverse Design with Dynamic Mode Decomposition Springer, 1966

Reference 1

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No event found in the named queried sources as of 2026-08-08T06:32:00.761636+00:00.

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Observation 5add5f66-21ed-4fc4-a5e9-bd39d2ed4287 · outbound

This paper cites Engineering design optimization.

Inverse Design with Dynamic Mode Decomposition Engineering design optimization

Reference 2

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Observation a5cd1718-ab5a-49cf-b4af-461b8cd29d8d · outbound

This paper cites Concepts and applications of finite element analysis.

Inverse Design with Dynamic Mode Decomposition Concepts and applications of finite element analysis

Reference 3

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Observation fa029e6c-cb29-4a7d-ab4f-4ff8d194cfa1 · outbound

This paper cites Model order reduction methods for geometrically nonlinear structures: a review of nonlinear techniques.

Inverse Design with Dynamic Mode Decomposition Model order reduction methods for geometrically nonlinear structures: a review of nonlinear techniques

Reference 4

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Observation 0abe56f1-85c5-48c3-908e-4571b9aff98e · outbound

This paper cites Modelling and design integration for engineering systems.

Inverse Design with Dynamic Mode Decomposition Modelling and design integration for engineering systems

Reference 5

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Observation 3a4ccdee-eeb4-4386-880f-266403929716 · outbound

This paper cites Physics-informed neural networks with hard constraints for inverse design.

Inverse Design with Dynamic Mode Decomposition Physics-informed neural networks with hard constraints for inverse design

Reference 6

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Observation 8a97cbda-cee6-4fe4-8b78-61fd254b54dd · outbound

This paper cites Fourier neural operator with learned defor- mations for pdes on general geometries.

Inverse Design with Dynamic Mode Decomposition Fourier neural operator with learned defor- mations for pdes on general geometries

Reference 7

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Observation ad8bb2b3-39e6-4c57-b58b-0e0d7cdb7e4e · outbound

This paper cites Physical Design using Differentiable Learned Simulators.

Inverse Design with Dynamic Mode Decomposition Physical Design using Differentiable Learned Simulators

Reference 8

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Observation 43da3c4c-fcec-462a-a144-e5b2338bcd71 · outbound

This paper cites Aerodynamic shape optimization using a novel optimizer based on machine learning techniques.

Inverse Design with Dynamic Mode Decomposition Aerodynamic shape optimization using a novel optimizer based on machine learning techniques

Reference 9

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Observation 2f4341d2-3507-4fd9-9dfd-900516d0646a · outbound

This paper cites Finite difference methods for ordinary and partial differential equations: steady-state and time- dependent problems.

Inverse Design with Dynamic Mode Decomposition Finite difference methods for ordinary and partial differential equations: steady-state and time- dependent problems

Reference 10

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Observation f939a103-7f81-466b-a8b3-fd3b5f5cbcd3 · outbound

This paper cites Finite and boundary element methods in engineering.

Inverse Design with Dynamic Mode Decomposition Finite and boundary element methods in engineering

Reference 11

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Observation 41fe4bca-e576-4e61-860c-d71c0e907fd1 · outbound

This paper cites Optimization for engineering design: Algorithms and examples.

Inverse Design with Dynamic Mode Decomposition Optimization for engineering design: Algorithms and examples

Reference 12

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Observation c7242f96-e888-41a4-add4-a41944d3a96d · outbound

This paper cites Reduced order model based on principal component analysis for process simulation and optimization.

Inverse Design with Dynamic Mode Decomposition Reduced order model based on principal component analysis for process simulation and optimization

Reference 13

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No event found in the named queried sources as of 2026-08-08T06:32:00.761636+00:00.

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Observation bebf6e23-b583-4b93-b16b-74e56e426700 · outbound

This paper cites A pod-based reduced order design scheme for shape optimization of air vehicles.

Inverse Design with Dynamic Mode Decomposition A pod-based reduced order design scheme for shape optimization of air vehicles

Reference 14

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Observation 0816365f-910e-44ff-ba59-4372eaa02cb8 · outbound

This paper cites Recent advances in convolutional neural networks.

Inverse Design with Dynamic Mode Decomposition Recent advances in convolutional neural networks

Reference 15

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Observation 8f924fec-41c6-4ef3-bab2-ac31ea110a55 · outbound

This paper cites Reinforcement learning: An introduction.

Inverse Design with Dynamic Mode Decomposition Reinforcement learning: An introduction

Reference 16

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Observation 597f9e2e-99b0-4f5b-bf6a-7433f0854263 · outbound

This paper cites Deepmpc: Learning deep latent features for model predictive control.

Inverse Design with Dynamic Mode Decomposition Deepmpc: Learning deep latent features for model predictive control

Reference 17

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No event found in the named queried sources as of 2026-08-08T06:32:00.761636+00:00.

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Observation 091d4797-ac88-4c15-a6aa-3d1011cf28dc · outbound

This paper cites A general reinforcement learning algorithm that masters chess, shogi, and go through self-play.

Inverse Design with Dynamic Mode Decomposition A general reinforcement learning algorithm that masters chess, shogi, and go through self-play

Reference 18

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Observation 837173af-ba0f-4d49-8aa9-782682bf33ae · outbound

This paper cites Reinforcement learning in robotics: A survey.

Inverse Design with Dynamic Mode Decomposition Reinforcement learning in robotics: A survey

Reference 19

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Observation f9c68db2-979b-439c-9f0d-538c8a1ad46e · outbound

This paper cites Real-time neural mpc: Deep learning model predictive control for quadrotors and agile robotic platforms.

Inverse Design with Dynamic Mode Decomposition Real-time neural mpc: Deep learning model predictive control for quadrotors and agile robotic platforms

Reference 20

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No event found in the named queried sources as of 2026-08-08T06:32:00.761636+00:00.

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Observation 9d48a051-2473-44b4-be29-152ece15d4e9 · outbound

This paper cites Distilling free-form natural laws from experimental data.

Inverse Design with Dynamic Mode Decomposition Distilling free-form natural laws from experimental data

Reference 21

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Observation 6bbe743f-ce77-4781-b73c-b911c4ebe1bc · outbound

This paper cites Trans- formers in vision: A survey.

Inverse Design with Dynamic Mode Decomposition Trans- formers in vision: A survey

Reference 22

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No event found in the named queried sources as of 2026-08-08T06:32:00.761636+00:00.

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Observation d7a8974b-f6be-4661-90ea-b50669a738ca · outbound

This paper cites On the Opportunities and Risks of Foundation Models.

Inverse Design with Dynamic Mode Decomposition On the Opportunities and Risks of Foundation Models

Reference 23

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Observation 9dcf2121-0372-4a78-bf0d-07a38380fdd6 · outbound

This paper cites Transformers: State-of-the-art natural language processing.

Inverse Design with Dynamic Mode Decomposition Transformers: State-of-the-art natural language processing

Reference 24

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Observation f773edf6-049d-4204-90a5-72804504bf47 · outbound

This paper cites Squeezeformer: An efficient transformer for automatic speech recognition.

Inverse Design with Dynamic Mode Decomposition Squeezeformer: An efficient transformer for automatic speech recognition

Reference 25

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Observation cd6e4772-9798-4616-a950-a671e1150d1a · outbound

This paper cites Multifidelity deep neural operators for efficient learning of partial differential equations with application to fast inverse design of nanoscale heat transport.

Inverse Design with Dynamic Mode Decomposition Multifidelity deep neural operators for efficient learning of partial differential equations with application to fast inverse design of nanoscale heat transport

Reference 26

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Observation 673ce5d3-338f-45ce-a3fd-7a3f7ae24a55 · outbound

This paper cites Neural implicit flow: a mesh-agnostic dimensionality reduction 28 paradigm of spatio-temporal data.

Inverse Design with Dynamic Mode Decomposition Neural implicit flow: a mesh-agnostic dimensionality reduction 28 paradigm of spatio-temporal data

Reference 27

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Observation 9d2d3861-5b9e-4774-a09d-194db4bc5d7b · outbound

This paper cites Deep neural operators as accurate surrogates for shape optimization.

Inverse Design with Dynamic Mode Decomposition Deep neural operators as accurate surrogates for shape optimization

Reference 28

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No event found in the named queried sources as of 2026-08-08T06:32:00.761636+00:00.

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Observation e035ca0d-54a0-4de3-a33b-b0b493ca4e2e · outbound

This paper cites On universal approximation and error bounds for fourier neural operators.

Inverse Design with Dynamic Mode Decomposition On universal approximation and error bounds for fourier neural operators

Reference 29

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No event found in the named queried sources as of 2026-08-08T06:32:00.761636+00:00.

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Observation 5b412657-d3e9-4d2e-b857-737b97bedadc · outbound

This paper cites A comprehensive and fair comparison of two neural operators (with practical extensions) based on fair data.

Inverse Design with Dynamic Mode Decomposition A comprehensive and fair comparison of two neural operators (with practical extensions) based on fair data

Reference 30

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Observation f0958c1b-0b49-4880-969d-bd0cbaf26e69 · outbound

This paper cites Blending neural operators and relaxation methods in pde numerical solvers.

Inverse Design with Dynamic Mode Decomposition Blending neural operators and relaxation methods in pde numerical solvers

Reference 31

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raw_fallback, observed 2026-08-07T21:24:02.890454Z

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No event found in the named queried sources as of 2026-08-08T06:32:00.761636+00:00.

source=pdf_text observed=2026-08-07T21:24:02.426619Z digest=sha256:3b4a47e96579a9d09f96e5ff3272599835aa09c1e025311bdc50bc842e1e8445

Observation f5bb39c4-456d-4843-b2f5-2642e59fb919 · outbound

This paper cites Data-driven science and engineering: Machine learning, dynamical systems, and control.

Inverse Design with Dynamic Mode Decomposition Data-driven science and engineering: Machine learning, dynamical systems, and control

Reference 32

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source=pdf_text observed=2026-08-07T21:24:02.430123Z digest=sha256:03bda4d1adec74ded3c7fe6aec3a34d6659896b30dd9c2b91bafd70303711768

Observation d24e0d14-77a7-45fd-b195-9e8c677c40a2 · outbound

This paper cites Dynamic mode decomposition: Theory and applications.

Inverse Design with Dynamic Mode Decomposition Dynamic mode decomposition: Theory and applications

Reference 33

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raw_fallback, observed 2026-08-07T21:24:02.873316Z

Source-reported events for the cited work

No event found in the named queried sources as of 2026-08-08T06:32:00.761636+00:00.

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Observation cadd4166-6df5-46b8-be7a-e35a9abb98dc · outbound

This paper cites Dynamic mode decomposition with control.

Inverse Design with Dynamic Mode Decomposition Dynamic mode decomposition with control

Reference 34

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source=pdf_text observed=2026-08-07T21:24:02.437136Z digest=sha256:ec746b49b50effd91036ad7b18c1d806fe77b7f89a9358bf859a753413764181

Observation 613cb1d3-3007-4071-893a-5cd43f0869e1 · outbound

This paper cites Deep learning for universal linear embeddings of nonlinear dynamics.

Inverse Design with Dynamic Mode Decomposition Deep learning for universal linear embeddings of nonlinear dynamics

Reference 35

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source=pdf_text observed=2026-08-07T21:24:02.440707Z digest=sha256:44bd2e241b05190a40b8edaff5dadcfa2b5b578558bc47a4f4305246b1012f34

Observation c43644a1-7a41-43c9-8ec8-eab84fa60b73 · outbound

This paper cites Dynamic mode decomposition and reconstruction of tip leakage vortex in a mixed flow pump as turbine at pump mode.

Inverse Design with Dynamic Mode Decomposition Dynamic mode decomposition and reconstruction of tip leakage vortex in a mixed flow pump as turbine at pump mode

Reference 36

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raw_fallback, observed 2026-08-07T21:24:02.850141Z

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No event found in the named queried sources as of 2026-08-08T06:32:00.761636+00:00.

source=pdf_text observed=2026-08-07T21:24:02.444349Z digest=sha256:92193c0cba4b5c6e6c9b9f832a4cb255701c68a88d330fcc5260c6a1eed24815

Observation ef30a5d6-3350-433d-be18-4400d844d90b · outbound

This paper cites A survey of projection-based model reduction methods for parametric dynamical systems.

Inverse Design with Dynamic Mode Decomposition A survey of projection-based model reduction methods for parametric dynamical systems

Reference 37

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Source-reported events for the cited work

Unavailable: canonical work link unavailable.

source=pdf_text observed=2026-08-07T21:24:02.448742Z digest=sha256:27e929800183875cacd2f47e622a286876b3ba37d13add79d4bf2a098552f28f

Observation 1ccebc6c-2bba-49ff-90fa-5e17b26addef · outbound

This paper cites Compressed sensing and dynamic mode decomposition.

Inverse Design with Dynamic Mode Decomposition Compressed sensing and dynamic mode decomposition

Reference 38

Resolution
verified fuzzy
raw_fallback, observed 2026-08-07T21:24:02.832083Z

Source-reported events for the cited work

No event found in the named queried sources as of 2026-08-08T06:32:00.761636+00:00.

source=pdf_text observed=2026-08-07T21:24:02.452299Z digest=sha256:42134b921954b66050409d312ae5198d25d4488ffaafe5a3976248adc245a1d9

Observation 0c0ebc1f-081e-49f0-bc18-50a496d1a731 · outbound

This paper cites Applied koopman theory for partial differential equations and data-driven modeling of spatio-temporal systems.

Inverse Design with Dynamic Mode Decomposition Applied koopman theory for partial differential equations and data-driven modeling of spatio-temporal systems

Reference 39

Resolution
verified fuzzy
raw_fallback, observed 2026-08-07T21:24:02.820301Z

Source-reported events for the cited work

No event found in the named queried sources as of 2026-08-08T06:32:00.761636+00:00.

source=pdf_text observed=2026-08-07T21:24:02.455870Z digest=sha256:5ce8d182b22b64ebc82f12647b470ed36182260b87c1071eb1d7452c2dc176ac

Observation 0a0373a7-f35a-4add-ad8c-1414222f3717 · outbound

This paper cites Design of nonlinear systems in the frequency domain: an output frequency response function-based approach.

Inverse Design with Dynamic Mode Decomposition Design of nonlinear systems in the frequency domain: an output frequency response function-based approach

Reference 40

Resolution
verified fuzzy
raw_fallback, observed 2026-08-07T21:24:02.808790Z

Source-reported events for the cited work

No event found in the named queried sources as of 2026-08-08T06:32:00.761636+00:00.

source=pdf_text observed=2026-08-07T21:24:02.459332Z digest=sha256:b612b01206f61c816be891c72beefa9c00deeea27f942f5dc5a663f4d285377e

Observation d65d8020-f382-4953-a9c2-c03fb527690f · outbound

This paper cites Design of a morphing airfoil using aerodynamic shape optimization.

Inverse Design with Dynamic Mode Decomposition Design of a morphing airfoil using aerodynamic shape optimization

Reference 41

Resolution
verified fuzzy
raw_fallback, observed 2026-08-07T21:24:02.797379Z

Source-reported events for the cited work

No event found in the named queried sources as of 2026-08-08T06:32:00.761636+00:00.

source=pdf_text observed=2026-08-07T21:24:02.462911Z digest=sha256:0d6f4b475ad299840015d86e2351b984782d6cc6e314a3af3bdb2c0edeef044c

Observation 82d95a52-6691-457f-9081-ac9070df13f2 · outbound

This paper cites Airfoil optimisation for vertical-axis wind turbines with variable pitch.

Inverse Design with Dynamic Mode Decomposition Airfoil optimisation for vertical-axis wind turbines with variable pitch

Reference 42

Resolution
verified fuzzy
raw_fallback, observed 2026-08-07T21:24:02.786546Z

Source-reported events for the cited work

No event found in the named queried sources as of 2026-08-08T06:32:00.761636+00:00.

source=pdf_text observed=2026-08-07T21:24:02.466756Z digest=sha256:52ec93f967ce02556a2c6c204bba6dded0201b052f0fa7bf656203fe3cdd4141

Observation b16ea8df-af02-4444-a067-0472f133075d · outbound

This paper cites Bagging, optimized dynamic mode decomposition for robust, stable forecasting with spatial and temporal uncertainty quantification.

Inverse Design with Dynamic Mode Decomposition Bagging, optimized dynamic mode decomposition for robust, stable forecasting with spatial and temporal uncertainty quantification

Reference 43

Resolution
verified fuzzy
raw_fallback, observed 2026-08-07T21:24:02.775956Z

Source-reported events for the cited work

No event found in the named queried sources as of 2026-08-08T06:32:00.761636+00:00.

source=pdf_text observed=2026-08-07T21:24:02.470263Z digest=sha256:f75b2256727ad8eca8dfaf9588dcbff797b56995a74ff799900aba4544ea12ac

Observation 30f41704-1ebb-4231-a9ae-8729ab09df06 · outbound

This paper cites Finding structure with randomness: Probabilistic algorithms for constructing approximate matrix decompositions.

Inverse Design with Dynamic Mode Decomposition Finding structure with randomness: Probabilistic algorithms for constructing approximate matrix decompositions

Reference 44

Resolution
unresolved
no resolver link, observed 2026-08-07T21:24:02.474060Z

Source-reported events for the cited work

Unavailable: canonical work link unavailable.

source=pdf_text observed=2026-08-07T21:24:02.474060Z digest=sha256:afabb496b37e447aa515365d6c90f8f70663d21c7dd6dbecfcb761a079f2d4fd

Observation 4c6b16dd-cfcf-4c74-829e-480b1f6fd7cc · outbound

This paper cites pylom: A hpc open source reduced order model suite for fluid dynamics applications.

Inverse Design with Dynamic Mode Decomposition pylom: A hpc open source reduced order model suite for fluid dynamics applications

Reference 45

Resolution
verified fuzzy
raw_fallback, observed 2026-08-07T21:24:02.759273Z

Source-reported events for the cited work

No event found in the named queried sources as of 2026-08-08T06:32:00.761636+00:00.

source=pdf_text observed=2026-08-07T21:24:02.477415Z digest=sha256:606d70709b7bfb4c942b6e401bf88456f377470b127a9241771034c50f2268e3

Observation 510733b2-1d41-4c7b-897e-0cc6d84b4f52 · outbound

This paper cites Learning the solution operator of parametric partial differential equations with physics-informed deeponets.

Inverse Design with Dynamic Mode Decomposition Learning the solution operator of parametric partial differential equations with physics-informed deeponets

Reference 46

Resolution
unresolved
no resolver link, observed 2026-08-07T21:24:02.481596Z

Source-reported events for the cited work

Unavailable: canonical work link unavailable.

source=pdf_text observed=2026-08-07T21:24:02.481596Z digest=sha256:f8a9789d7e8e856ed6f341a6f7e99200c67f5a9593568f036fe0a643e6d60750

Observation ea65694d-c503-4a8b-91de-37e15d9fe891 · outbound

This paper cites Theoretical study of the effects of nonlinear viscous damping on vibration isolation of sdof systems.

Inverse Design with Dynamic Mode Decomposition Theoretical study of the effects of nonlinear viscous damping on vibration isolation of sdof systems

Reference 47

Resolution
verified fuzzy
raw_fallback, observed 2026-08-07T21:24:02.741455Z

Source-reported events for the cited work

No event found in the named queried sources as of 2026-08-08T06:32:00.761636+00:00.

source=pdf_text observed=2026-08-07T21:24:02.484906Z digest=sha256:351adb033a1b472905ffe5436b4a6b854a37008111e10f1278f82897b9de2156

Observation 44af6365-4aa8-4676-81fd-4cf4e77b063b · outbound

This paper cites Beneficial effects of antisymmetric nonlinear damping with application to energy harvesting and vibration isolation under general inputs.

Inverse Design with Dynamic Mode Decomposition Beneficial effects of antisymmetric nonlinear damping with application to energy harvesting and vibration isolation under general inputs

Reference 48

Resolution
verified fuzzy
raw_fallback, observed 2026-08-07T21:24:02.730059Z

Source-reported events for the cited work

No event found in the named queried sources as of 2026-08-08T06:32:00.761636+00:00.

source=pdf_text observed=2026-08-07T21:24:02.488377Z digest=sha256:7178d9c4927b4f30519221384f547d5ac35df5f05a0e852e64fb434e378504c4

Observation e08cb379-04ec-40c2-a49a-324635dc88dc · outbound

This paper cites Learning koopman invariant subspaces for dynamic mode decomposition.

Inverse Design with Dynamic Mode Decomposition Learning koopman invariant subspaces for dynamic mode decomposition

Reference 49

Resolution
verified fuzzy
raw_fallback, observed 2026-08-07T21:24:02.719764Z

Source-reported events for the cited work

No event found in the named queried sources as of 2026-08-08T06:32:00.761636+00:00.

source=pdf_text observed=2026-08-07T21:24:02.491709Z digest=sha256:33cd03e16d7a40e6c860763d33b503e9bb089e04c3edce48be0d22973f8a7b65

Observation efab16ee-1c54-444e-bfb5-3f3b4e403c74 · outbound

This paper cites Response surface methodology.

Inverse Design with Dynamic Mode Decomposition Response surface methodology

Reference 50

Resolution
verified fuzzy
raw_fallback, observed 2026-08-07T21:24:02.708483Z

Source-reported events for the cited work

No event found in the named queried sources as of 2026-08-08T06:32:00.761636+00:00.

source=pdf_text observed=2026-08-07T21:24:02.495083Z digest=sha256:37ac5070f5347ddc65bd9997620a8dd01effabdafec4fae6445e406b754b36d1

Observation 7f5a83b4-80f4-428c-9fe9-8d44adde2648 · outbound

This paper cites Robustness issues of the best linear approximation of a nonlinear system.

Inverse Design with Dynamic Mode Decomposition Robustness issues of the best linear approximation of a nonlinear system

Reference 51

Resolution
verified fuzzy
raw_fallback, observed 2026-08-07T21:24:02.696329Z

Source-reported events for the cited work

No event found in the named queried sources as of 2026-08-08T06:32:00.761636+00:00.

source=pdf_text observed=2026-08-07T21:24:02.498612Z digest=sha256:4b9db484553fcf37274a07d852023fae270d9c191bb6025a39ebc27f1cfb2f7e

Observation 9accdf66-37bf-4297-a09a-611dad5d9d21 · outbound

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

Inverse Design with Dynamic Mode Decomposition Parametric dynamic mode decomposition for reduced order modeling

Reference 52

Resolution
verified fuzzy
raw_fallback, observed 2026-08-07T21:24:02.684618Z

Source-reported events for the cited work

No event found in the named queried sources as of 2026-08-08T06:32:00.761636+00:00.

source=pdf_text observed=2026-08-07T21:24:02.502258Z digest=sha256:da6f1631a20e5d3e861d3bf8c74d4dfffe81da329e075fafa557b994f2293341

Observation 091e7460-f933-449d-8894-d5b55681e248 · outbound

This paper cites A dynamic mode decomposition extension for the forecasting of parametric dynamical systems.

Inverse Design with Dynamic Mode Decomposition A dynamic mode decomposition extension for the forecasting of parametric dynamical systems

Reference 53

Resolution
verified fuzzy
raw_fallback, observed 2026-08-07T21:24:02.672348Z

Source-reported events for the cited work

No event found in the named queried sources as of 2026-08-08T06:32:00.761636+00:00.

source=pdf_text observed=2026-08-07T21:24:02.505603Z digest=sha256:f7b39a8ddc939514d65c5f7f32b5258d00cd00bcd846a497625ffb5130d80ffb

Observation 62a3ebb0-9ed6-4017-919c-15d90168ac67 · outbound

This paper cites The optimal hard threshold for singular values is 4 / √.

Inverse Design with Dynamic Mode Decomposition The optimal hard threshold for singular values is 4 / √

Reference 54

Resolution
verified fuzzy
raw_fallback, observed 2026-08-07T21:24:02.660187Z

Source-reported events for the cited work

No event found in the named queried sources as of 2026-08-08T06:32:00.761636+00:00.

source=pdf_text observed=2026-08-07T21:24:02.508887Z digest=sha256:fe815acce65efbfe0e46334cc8ac0911c1f13b77187dc680baf0cddcf0385a92

Observation 49f4ab59-340d-4cbc-8fda-88fe0bb97b43 · outbound

This paper cites an unresolved cited work.

Inverse Design with Dynamic Mode Decomposition Unresolved cited work

Reference 55

Resolution
unresolved
raw_fallback, observed 2026-08-07T21:24:02.648426Z

Source-reported events for the cited work

No event found in the named queried sources as of 2026-08-08T06:32:00.761636+00:00.

source=pdf_text observed=2026-08-07T21:24:02.512493Z digest=sha256:bc08d598505a9abe8a4d277b39787872c730514da004d26d0719d48c5d239c2d

Observation 09c8f70f-a131-4c17-8af7-76ff116a9a3d · outbound

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

Inverse Design with Dynamic Mode Decomposition Dynamic mode decomposition of numerical and experimental data

Reference 56

Resolution
unresolved
no resolver link, observed 2026-08-07T21:24:02.516026Z

Source-reported events for the cited work

Unavailable: canonical work link unavailable.

source=pdf_text observed=2026-08-07T21:24:02.516026Z digest=sha256:48142a77fd927b93c06584cfdfbfa7326974412289476508fa701a12c38e43f2

Observation 809fc084-b87d-44ee-b95d-4f16ea15601b · outbound

This paper cites A better measure of relative prediction accuracy for model selection and model estimation.

Inverse Design with Dynamic Mode Decomposition A better measure of relative prediction accuracy for model selection and model estimation

Reference 57

Resolution
verified fuzzy
raw_fallback, observed 2026-08-07T21:24:02.629876Z

Source-reported events for the cited work

No event found in the named queried sources as of 2026-08-08T06:32:00.761636+00:00.

source=pdf_text observed=2026-08-07T21:24:02.519324Z digest=sha256:0f6c0a879c99b0cd95e0c1074108ecb0d0ad038e699e13d41516b14d908c403c

Observation 49e3f201-5ab8-4c0b-bc60-0d697b67c210 · outbound

This paper cites Lattice-boltzmann method for complex flows.

Inverse Design with Dynamic Mode Decomposition Lattice-boltzmann method for complex flows

Reference 58

Resolution
verified fuzzy
raw_fallback, observed 2026-08-07T21:24:02.618410Z

Source-reported events for the cited work

No event found in the named queried sources as of 2026-08-08T06:32:00.761636+00:00.

source=pdf_text observed=2026-08-07T21:24:02.522791Z digest=sha256:572b49316f34197cdc1a51ddd35ae722c911848c8ae4921dae1bf494614d86aa

Observation 424d919c-150b-485e-9a40-6c16d1dabb36 · outbound

This paper cites The design of nonlinear damped building isolation systems by using mobility analysis.

Inverse Design with Dynamic Mode Decomposition The design of nonlinear damped building isolation systems by using mobility analysis

Reference 59

Resolution
verified fuzzy
raw_fallback, observed 2026-08-07T21:24:02.607002Z

Source-reported events for the cited work

No event found in the named queried sources as of 2026-08-08T06:32:00.761636+00:00.

source=pdf_text observed=2026-08-07T21:24:02.526322Z digest=sha256:832a1619c3ea9859525137ba61a68069611d271c3a441b409b1c3f7cc82ff4b6

Observation 6a430474-ee1f-4d30-a072-f4f09f5f3891 · outbound

This paper cites The 2d lid-driven cavity problem revisited.

Inverse Design with Dynamic Mode Decomposition The 2d lid-driven cavity problem revisited

Reference 60

Resolution
verified fuzzy
raw_fallback, observed 2026-08-07T21:24:02.596106Z

Source-reported events for the cited work

No event found in the named queried sources as of 2026-08-08T06:32:00.761636+00:00.

source=pdf_text observed=2026-08-07T21:24:02.529999Z digest=sha256:ac6cc3dd9b336e4e9e99dc68498e8eaf4bede2338c2ef23d70b7660412d5f04e

Observation dee4cafc-d455-48b4-940a-b9de1ca247ac · outbound

This paper cites Output frequency characteristics of nonlinear systems.

Inverse Design with Dynamic Mode Decomposition Output frequency characteristics of nonlinear systems

Reference 61

Resolution
verified fuzzy
raw_fallback, observed 2026-08-07T21:24:02.584568Z

Source-reported events for the cited work

No event found in the named queried sources as of 2026-08-08T06:32:00.761636+00:00.

source=pdf_text observed=2026-08-07T21:24:02.533408Z digest=sha256:64219edc1708e91f3f76c0c49bff1c15b9edabce96000fd24d4dbd6cc7b8caa4

Pith citing papers

No inbound Pith citation observations are available.