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Paper Citation Record · LEDGER

Data-driven nonlinear modal identification of nonlinear dynamical systems with physics-constrained Normalizing Flows

As of 13 August 2026, this Paper Citation Record lists 58 of 58 outbound references and 0 inbound Pith citation observations for arXiv:2501.14124.

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

pith.paper-citation-record.v1
2501.14124 v1

Coverage vector

measured 58 of 58 reference resolution

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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

58 of 58 outbound references displayed

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External citation measurements

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Outbound references

Observation 4757fe2e-2cd0-4397-9ae9-911dc371313f · outbound

This paper cites John Mallinckrodt, and Susan McKay.

Data-driven nonlinear modal identification of nonlinear dynamical systems with physics-constrained Normalizing Flows John Mallinckrodt, and Susan McKay

Reference 1

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This paper cites Modal Analysis Theory and Testing.

Data-driven nonlinear modal identification of nonlinear dynamical systems with physics-constrained Normalizing Flows Modal Analysis Theory and Testing

Reference 2

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This paper cites Nonlinear nor- mal modes and spectral submanifolds: existence, uniqueness and use in model reduction.

Data-driven nonlinear modal identification of nonlinear dynamical systems with physics-constrained Normalizing Flows Nonlinear nor- mal modes and spectral submanifolds: existence, uniqueness and use in model reduction

Reference 3

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This paper cites Experimen- tal modal analysis for dynamic models of space- craft.

Data-driven nonlinear modal identification of nonlinear dynamical systems with physics-constrained Normalizing Flows Experimen- tal modal analysis for dynamic models of space- craft

Reference 4

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Data-driven nonlinear modal identification of nonlinear dynamical systems with physics-constrained Normalizing Flows Unresolved cited work

Reference 5

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This paper cites Touz´ e, M.

Data-driven nonlinear modal identification of nonlinear dynamical systems with physics-constrained Normalizing Flows Touz´ e, M

Reference 6

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This paper cites Bladh, C.

Data-driven nonlinear modal identification of nonlinear dynamical systems with physics-constrained Normalizing Flows Bladh, C

Reference 7

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Data-driven nonlinear modal identification of nonlinear dynamical systems with physics-constrained Normalizing Flows Unresolved cited work

Reference 8

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This paper cites Modern solutions for ground vibra- tion testing of large aircraft.

Data-driven nonlinear modal identification of nonlinear dynamical systems with physics-constrained Normalizing Flows Modern solutions for ground vibra- tion testing of large aircraft

Reference 9

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Data-driven nonlinear modal identification of nonlinear dynamical systems with physics-constrained Normalizing Flows Unresolved cited work

Reference 10

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This paper cites Kerschen, M.

Data-driven nonlinear modal identification of nonlinear dynamical systems with physics-constrained Normalizing Flows Kerschen, M

Reference 11

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This paper cites Independent component analysis: a tutorial introduction.

Data-driven nonlinear modal identification of nonlinear dynamical systems with physics-constrained Normalizing Flows Independent component analysis: a tutorial introduction

Reference 12

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Data-driven nonlinear modal identification of nonlinear dynamical systems with physics-constrained Normalizing Flows Physical interpretation of independent compo- nent analysis in structural dynamics

Reference 13

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Data-driven nonlinear modal identification of nonlinear dynamical systems with physics-constrained Normalizing Flows Dynamic mode decom- position: data-driven modeling of complex systems

Reference 14

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Data-driven nonlinear modal identification of nonlinear dynamical systems with physics-constrained Normalizing Flows Dynamic mode decomposition of numerical and experimental data

Reference 15

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Data-driven nonlinear modal identification of nonlinear dynamical systems with physics-constrained Normalizing Flows Applications of the dynamic mode decom- position

Reference 16

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Data-driven nonlinear modal identification of nonlinear dynamical systems with physics-constrained Normalizing Flows Spectral properties of dynamical sys- tems, model reduction and decompositions

Reference 17

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Data-driven nonlinear modal identification of nonlinear dynamical systems with physics-constrained Normalizing Flows Analysis of Fluid Flows via Spectral Properties of the Koopman Operator

Reference 18

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Reference 19

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Data-driven nonlinear modal identification of nonlinear dynamical systems with physics-constrained Normalizing Flows Unresolved cited work

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Data-driven nonlinear modal identification of nonlinear dynamical systems with physics-constrained Normalizing Flows Nonlinear nor- mal modes, part i: A useful framework for the struc- tural dynamicist

Reference 21

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Data-driven nonlinear modal identification of nonlinear dynamical systems with physics-constrained Normalizing Flows Analysis and identification of linear and nonlinear normal modes in vibrating systems

Reference 22

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Data-driven nonlinear modal identification of nonlinear dynamical systems with physics-constrained Normalizing Flows A higher order approximation for non-linear normal modes in two degree of free- dom systems

Reference 23

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Data-driven nonlinear modal identification of nonlinear dynamical systems with physics-constrained Normalizing Flows Non-linear normal modes and invariant manifolds

Reference 24

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Data-driven nonlinear modal identification of nonlinear dynamical systems with physics-constrained Normalizing Flows Normal modes for non-linear vibratory systems

Reference 25

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Data-driven nonlinear modal identification of nonlinear dynamical systems with physics-constrained Normalizing Flows Unresolved cited work

Reference 26

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Data-driven nonlinear modal identification of nonlinear dynamical systems with physics-constrained Normalizing Flows Kuether and Matthew S

Reference 27

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Data-driven nonlinear modal identification of nonlinear dynamical systems with physics-constrained Normalizing Flows Peeters, R

Reference 28

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Data-driven nonlinear modal identification of nonlinear dynamical systems with physics-constrained Normalizing Flows Automated computation of autonomous spectral submanifolds for nonlinear modal analy- sis

Reference 29

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Data-driven nonlinear modal identification of nonlinear dynamical systems with physics-constrained Normalizing Flows Universal approximation of an unknown mapping and its derivatives using multilayer feed- forward networks

Reference 30

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This paper cites Threat of Ad- versarial Attacks on Deep Learning in Computer Vision: A Survey, feb 2018.

Data-driven nonlinear modal identification of nonlinear dynamical systems with physics-constrained Normalizing Flows Threat of Ad- versarial Attacks on Deep Learning in Computer Vision: A Survey, feb 2018

Reference 31

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Data-driven nonlinear modal identification of nonlinear dynamical systems with physics-constrained Normalizing Flows Nonlinear modal anal- ysis via non-parametric machine learning tools

Reference 32

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Data-driven nonlinear modal identification of nonlinear dynamical systems with physics-constrained Normalizing Flows Worden and P

Reference 33

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Data-driven nonlinear modal identification of nonlinear dynamical systems with physics-constrained Normalizing Flows Learning deep neural network representations for koopman operators of nonlinear dynamical sys- tems

Reference 34

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Observation 90b3834c-8379-4ec0-9f54-a45a40ca22c9 · outbound

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Data-driven nonlinear modal identification of nonlinear dynamical systems with physics-constrained Normalizing Flows Deep learning for universal linear em- beddings of nonlinear dynamics

Reference 35

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verified fuzzy
raw_fallback, observed 2026-08-10T15:26:18.228227Z

Source-reported events for the cited work

No event found in the named queried sources as of 2026-08-12T06:34:41.77262+00:00.

source=pdf_text observed=2026-08-10T15:26:17.749872Z digest=sha256:c81e7b38ae9f26e962290e20e0f75fee2d18903830a53ff0236a0378c8ccdf96

Observation 5d0b8952-cd1e-4cdc-b58b-a835ba5cdc98 · outbound

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

Data-driven nonlinear modal identification of nonlinear dynamical systems with physics-constrained Normalizing Flows Learning koopman invariant subspaces for dynamic mode decomposition

Reference 36

Resolution
verified fuzzy
raw_fallback, observed 2026-08-10T15:26:18.214733Z

Source-reported events for the cited work

No event found in the named queried sources as of 2026-08-12T06:34:41.77262+00:00.

source=pdf_text observed=2026-08-10T15:26:17.754907Z digest=sha256:c5dc0bd881a94cf3ad2fbbf5fb63c4eca5e816cebbcce9be0fe82425b867785b

Observation a6245b5e-d8ab-49a0-ba8f-a75541b6a1ec · outbound

This paper cites Data-driven iden- tification of nonlinear normal modes via physics- integrated deep learning.

Data-driven nonlinear modal identification of nonlinear dynamical systems with physics-constrained Normalizing Flows Data-driven iden- tification of nonlinear normal modes via physics- integrated deep learning

Reference 37

Resolution
verified fuzzy
raw_fallback, observed 2026-08-10T15:26:18.201184Z

Source-reported events for the cited work

No event found in the named queried sources as of 2026-08-12T06:34:41.77262+00:00.

source=pdf_text observed=2026-08-10T15:26:17.759601Z digest=sha256:b8bd12ba49fd597893b007d25c0bb73d35d6072b910ab84bf1a92e0f765ea87f

Observation 78e057e9-251a-4082-92bb-da0dfbb2e090 · outbound

This paper cites Imitationflow: Learning deep sta- ble stochastic dynamic systems by normalizing flows.

Data-driven nonlinear modal identification of nonlinear dynamical systems with physics-constrained Normalizing Flows Imitationflow: Learning deep sta- ble stochastic dynamic systems by normalizing flows

Reference 38

Resolution
verified fuzzy
raw_fallback, observed 2026-08-10T15:26:18.187307Z

Source-reported events for the cited work

No event found in the named queried sources as of 2026-08-12T06:34:41.77262+00:00.

source=pdf_text observed=2026-08-10T15:26:17.765230Z digest=sha256:668f25e562a80b2eb709a9b9e9d6c88c133d4b46d3430f345861eed68c538ec7

Observation 3ffd620c-6d33-4156-b8d2-e2382f98988d · outbound

This paper cites Normalizing flows: An introduction and review of current methods.

Data-driven nonlinear modal identification of nonlinear dynamical systems with physics-constrained Normalizing Flows Normalizing flows: An introduction and review of current methods

Reference 39

Resolution
verified fuzzy
raw_fallback, observed 2026-08-10T15:26:18.174074Z

Source-reported events for the cited work

No event found in the named queried sources as of 2026-08-12T06:34:41.77262+00:00.

source=pdf_text observed=2026-08-10T15:26:17.769682Z digest=sha256:7bd83320b367f588c70c5e60ae38a5cee108cbba908af51c4eac977263872269

Observation 993e27ed-1922-4621-b421-19c244ad6b48 · outbound

This paper cites Learning stable normalizing- 18 Abdolvahhab Rostamijavanani et al.

Data-driven nonlinear modal identification of nonlinear dynamical systems with physics-constrained Normalizing Flows Learning stable normalizing- 18 Abdolvahhab Rostamijavanani et al

Reference 40

Resolution
verified fuzzy
raw_fallback, observed 2026-08-10T15:26:18.160327Z

Source-reported events for the cited work

No event found in the named queried sources as of 2026-08-12T06:34:41.77262+00:00.

source=pdf_text observed=2026-08-10T15:26:17.774264Z digest=sha256:e1350be265f6b47c05029c39583c9a5639fdd7ac105bb468dc2a4d9fda9dedb8

Observation df49c952-ddc9-499c-984a-cef04bbd4ef2 · outbound

This paper cites Probabilistic sur- rogate modeling of unsteady fluid dynamics using deep graph normalizing flows.Bulletin of the Amer- ican Physical Society, 2022.

Data-driven nonlinear modal identification of nonlinear dynamical systems with physics-constrained Normalizing Flows Probabilistic sur- rogate modeling of unsteady fluid dynamics using deep graph normalizing flows.Bulletin of the Amer- ican Physical Society, 2022

Reference 41

Resolution
verified fuzzy
raw_fallback, observed 2026-08-10T15:26:18.146733Z

Source-reported events for the cited work

No event found in the named queried sources as of 2026-08-12T06:34:41.77262+00:00.

source=pdf_text observed=2026-08-10T15:26:17.778439Z digest=sha256:853596396e1c193d5f3bbc44e385170d4ddbf52bfe8c8c699b797af4fcd12ac4

Observation 8efb4e6c-4b88-4d6f-a8a9-4f19828bbc5e · outbound

This paper cites NICE: Non-linear Independent Components Estimation.

Data-driven nonlinear modal identification of nonlinear dynamical systems with physics-constrained Normalizing Flows NICE: Non-linear Independent Components Estimation

Reference 42

Resolution
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no resolver link, observed 2026-08-10T15:26:17.783773Z

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Unavailable: canonical work link unavailable.

source=pdf_text observed=2026-08-10T15:26:17.783773Z digest=sha256:bc6912a5acb8b68bf7fe33cca384906f549e9b6d87f6c4e2e80c38fc7a64b109

Observation 046cc418-6740-40c6-a4f3-cadee0d3a0e2 · outbound

This paper cites Density estimation using Real NVP.

Data-driven nonlinear modal identification of nonlinear dynamical systems with physics-constrained Normalizing Flows Density estimation using Real NVP

Reference 43

Resolution
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no resolver link, observed 2026-08-10T15:26:17.788265Z

Source-reported events for the cited work

Unavailable: canonical work link unavailable.

source=pdf_text observed=2026-08-10T15:26:17.788265Z digest=sha256:f0af1fe95f6f36435073f869b55669944a2bd529c9e89f7566dd217e377d6826

Observation 021e702b-29c7-44e5-b1fb-d82204d77616 · outbound

This paper cites Normalizing flows for probabilistic modeling and inference.

Data-driven nonlinear modal identification of nonlinear dynamical systems with physics-constrained Normalizing Flows Normalizing flows for probabilistic modeling and inference

Reference 44

Resolution
verified fuzzy
raw_fallback, observed 2026-08-10T15:26:18.133280Z

Source-reported events for the cited work

No event found in the named queried sources as of 2026-08-12T06:34:41.77262+00:00.

source=pdf_text observed=2026-08-10T15:26:17.793469Z digest=sha256:b0eba44a7ca690586a18bd1fd48b6cad625ca3b6074a3f6de19939cec2c61d98

Observation 0ea216e4-d617-4162-910b-d7449c0ffc8d · outbound

This paper cites Generative adver- sarial networks.

Data-driven nonlinear modal identification of nonlinear dynamical systems with physics-constrained Normalizing Flows Generative adver- sarial networks

Reference 45

Resolution
verified fuzzy
raw_fallback, observed 2026-08-10T15:26:18.119519Z

Source-reported events for the cited work

No event found in the named queried sources as of 2026-08-12T06:34:41.77262+00:00.

source=pdf_text observed=2026-08-10T15:26:17.797923Z digest=sha256:b4821c2367a2da2f32689f4a45bede7706d29b27f01a17d02dacbd6df798e13e

Observation cc95769c-6efa-49e2-af7f-e0596367626f · outbound

This paper cites Auto-Encoding Variational Bayes.

Data-driven nonlinear modal identification of nonlinear dynamical systems with physics-constrained Normalizing Flows Auto-Encoding Variational Bayes

Reference 46

Resolution
unresolved
no resolver link, observed 2026-08-10T15:26:17.802261Z

Source-reported events for the cited work

Unavailable: canonical work link unavailable.

source=pdf_text observed=2026-08-10T15:26:17.802261Z digest=sha256:68e43e172207d0f1d32b41a41f26ff991b335ca6739f2fe8c6977a99c7491f16

Observation cc83f985-5fae-41d0-9e03-3ef3c84e279b · outbound

This paper cites Nathan Kutz, and Steven L.

Data-driven nonlinear modal identification of nonlinear dynamical systems with physics-constrained Normalizing Flows Nathan Kutz, and Steven L

Reference 47

Resolution
verified fuzzy
raw_fallback, observed 2026-08-10T15:26:18.105595Z

Source-reported events for the cited work

No event found in the named queried sources as of 2026-08-12T06:34:41.77262+00:00.

source=pdf_text observed=2026-08-10T15:26:17.807372Z digest=sha256:66eb27f744c60b5e9d4cc7097c0763d032c4964ca8ad8a1377baa452222ea684

Observation 95212b97-4af0-4dd8-97b0-81b8aa50d7e6 · outbound

This paper cites Understand- ing the difficulty of training deep feedforward neu- ral networks.

Data-driven nonlinear modal identification of nonlinear dynamical systems with physics-constrained Normalizing Flows Understand- ing the difficulty of training deep feedforward neu- ral networks

Reference 48

Resolution
verified fuzzy
raw_fallback, observed 2026-08-10T15:26:18.092019Z

Source-reported events for the cited work

No event found in the named queried sources as of 2026-08-12T06:34:41.77262+00:00.

source=pdf_text observed=2026-08-10T15:26:17.811625Z digest=sha256:c15443f29e67c81e8356069f89c47588416411aeba1e08bad32e221089935216

Observation e1fa35cd-d9e7-4698-8627-96c606a3b470 · outbound

This paper cites Non-stationary random response of mdof duffing systems.

Data-driven nonlinear modal identification of nonlinear dynamical systems with physics-constrained Normalizing Flows Non-stationary random response of mdof duffing systems

Reference 49

Resolution
verified fuzzy
raw_fallback, observed 2026-08-10T15:26:18.076809Z

Source-reported events for the cited work

No event found in the named queried sources as of 2026-08-12T06:34:41.77262+00:00.

source=pdf_text observed=2026-08-10T15:26:17.816492Z digest=sha256:9475146d1b554bef95d2b7ec26fe0172dfbc122d80dd792e163d70516d205e20

Observation a62112a7-e3d5-423e-8043-7d2e882191d5 · outbound

This paper cites On lyapunov control of the duffing equation.

Data-driven nonlinear modal identification of nonlinear dynamical systems with physics-constrained Normalizing Flows On lyapunov control of the duffing equation

Reference 50

Resolution
verified fuzzy
raw_fallback, observed 2026-08-10T15:26:18.062248Z

Source-reported events for the cited work

No event found in the named queried sources as of 2026-08-12T06:34:41.77262+00:00.

source=pdf_text observed=2026-08-10T15:26:17.820780Z digest=sha256:91227a5bb9cdf75fc2f90a36f936a1474c3474e08cdced080a2928f3e97a90cc

Observation bb6c65ed-1673-486a-8fe1-1a7d13229e54 · outbound

This paper cites Modal testing of nonlinear vibrat- ing structures based on nonlinear normal modes: Experimental demonstration.

Data-driven nonlinear modal identification of nonlinear dynamical systems with physics-constrained Normalizing Flows Modal testing of nonlinear vibrat- ing structures based on nonlinear normal modes: Experimental demonstration

Reference 51

Resolution
verified fuzzy
raw_fallback, observed 2026-08-10T15:26:18.048236Z

Source-reported events for the cited work

No event found in the named queried sources as of 2026-08-12T06:34:41.77262+00:00.

source=pdf_text observed=2026-08-10T15:26:17.825721Z digest=sha256:c2963675ed5c66564bad64a88962012d29c878c0179c850b8271bda3b98137f0

Observation 94f150b5-4881-43f8-bc74-43eff4ff0f07 · outbound

This paper cites Hidden fluid mechanics: Learning ve- locity and pressure fields from flow visualizations.

Data-driven nonlinear modal identification of nonlinear dynamical systems with physics-constrained Normalizing Flows Hidden fluid mechanics: Learning ve- locity and pressure fields from flow visualizations

Reference 52

Resolution
verified fuzzy
raw_fallback, observed 2026-08-10T15:26:18.033582Z

Source-reported events for the cited work

No event found in the named queried sources as of 2026-08-12T06:34:41.77262+00:00.

source=pdf_text observed=2026-08-10T15:26:17.830018Z digest=sha256:70d8df4701aab0aa04b19b5f8c65eaae637381704e4e8770761001b917210a9e

Observation 921d275d-e940-4052-a611-3147d83f9f0a · outbound

This paper cites Nonlinear mode decomposition with convolutional neural networks for fluid dynamics.

Data-driven nonlinear modal identification of nonlinear dynamical systems with physics-constrained Normalizing Flows Nonlinear mode decomposition with convolutional neural networks for fluid dynamics

Reference 53

Resolution
verified fuzzy
raw_fallback, observed 2026-08-10T15:26:18.018240Z

Source-reported events for the cited work

No event found in the named queried sources as of 2026-08-12T06:34:41.77262+00:00.

source=pdf_text observed=2026-08-10T15:26:17.834726Z digest=sha256:e6d98ee8a23f664e5d70cc5df4da1561e6beb924798afd5ccd1737cd94595520

Observation 2c861397-9954-49eb-8f31-06cf9145a86c · outbound

This paper cites Turbulence, coherent struc- tures, dynamical systems and symmetry.

Data-driven nonlinear modal identification of nonlinear dynamical systems with physics-constrained Normalizing Flows Turbulence, coherent struc- tures, dynamical systems and symmetry

Reference 54

Resolution
verified fuzzy
raw_fallback, observed 2026-08-10T15:26:18.000425Z

Source-reported events for the cited work

No event found in the named queried sources as of 2026-08-12T06:34:41.77262+00:00.

source=pdf_text observed=2026-08-10T15:26:17.838919Z digest=sha256:3e3cc7cf61a760066d50f4fbe242ffd9ee4a5d79874e2f033f17b324fd736f89

Observation cfa04955-46e2-46d7-8932-d729369e5989 · outbound

This paper cites Modal analysis of fluid flows: An overview.

Data-driven nonlinear modal identification of nonlinear dynamical systems with physics-constrained Normalizing Flows Modal analysis of fluid flows: An overview

Reference 55

Resolution
verified fuzzy
raw_fallback, observed 2026-08-10T15:26:17.984990Z

Source-reported events for the cited work

No event found in the named queried sources as of 2026-08-12T06:34:41.77262+00:00.

source=pdf_text observed=2026-08-10T15:26:17.843177Z digest=sha256:18beb4d54fece10cb5d26aab51f8b98143bb629a91ba0dd1dcb02363a7d207db

Observation 954e148d-e6aa-460f-8831-c28b8001b80b · outbound

This paper cites A geometric interpretation of darroch and ratcliff’s generalized iterative scaling.

Data-driven nonlinear modal identification of nonlinear dynamical systems with physics-constrained Normalizing Flows A geometric interpretation of darroch and ratcliff’s generalized iterative scaling

Reference 56

Resolution
verified fuzzy
raw_fallback, observed 2026-08-10T15:26:17.970086Z

Source-reported events for the cited work

No event found in the named queried sources as of 2026-08-12T06:34:41.77262+00:00.

source=pdf_text observed=2026-08-10T15:26:17.847605Z digest=sha256:9df08153465efcf73a4d1dbab0496c1a59a0c71be633c7af74d669175cb52cfa

Observation b345d5bd-2bf2-4b3e-bf3e-6229bbfa640e · outbound

This paper cites Ranking of smart building de- sign factors with efficient energy management sys- tems and renewable resources.

Data-driven nonlinear modal identification of nonlinear dynamical systems with physics-constrained Normalizing Flows Ranking of smart building de- sign factors with efficient energy management sys- tems and renewable resources

Reference 57

Resolution
verified fuzzy
raw_fallback, observed 2026-08-10T15:26:17.955739Z

Source-reported events for the cited work

No event found in the named queried sources as of 2026-08-12T06:34:41.77262+00:00.

source=pdf_text observed=2026-08-10T15:26:17.852008Z digest=sha256:e90cea2aff8fadf7ab0b4bb73ce19d4e0bea3c7712ebb500460af14602ee83a3

Observation 33a3678f-2b71-4363-b98a-41ea1ba77ef5 · outbound

This paper cites Smart materials in green ar- chitecture: The role of etfe and phase change ma- terials in sustainable building design.

Data-driven nonlinear modal identification of nonlinear dynamical systems with physics-constrained Normalizing Flows Smart materials in green ar- chitecture: The role of etfe and phase change ma- terials in sustainable building design

Reference 58

Resolution
verified fuzzy
raw_fallback, observed 2026-08-10T15:26:17.940821Z

Source-reported events for the cited work

No event found in the named queried sources as of 2026-08-12T06:34:41.77262+00:00.

source=pdf_text observed=2026-08-10T15:26:17.856280Z digest=sha256:0bc01aec06cbad046ee619449b02df879e3441522b6469190da06d0a8b166e26

Pith citing papers

No inbound Pith citation observations are available.