Paper Citation Record · LEDGER

An Improved Autoencoder Conjugacy Network to Learn Chaotic Maps

As of 20 August 2026, this Paper Citation Record lists 35 of 35 outbound references and 0 inbound Pith citation observations for arXiv:2507.09835.

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

pith.paper-citation-record.v1
2507.09835 v1

Coverage vector

measured 35 of 35 reference resolution

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measured 35 of 35 standing notices

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Pith citing papers itemized under the disclosed page cap.

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Reference resolution

35 of 35 outbound references displayed

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External citation measurements

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Outbound references

Observation 755d5507-ae01-4df3-a713-89e2a73a26c6 · outbound

This paper cites Extremes and recurrence in dynamical systems.

An Improved Autoencoder Conjugacy Network to Learn Chaotic Maps Extremes and recurrence in dynamical systems

Reference 1

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This paper cites an unresolved cited work.

An Improved Autoencoder Conjugacy Network to Learn Chaotic Maps Unresolved cited work

Reference 2

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Observation 645fe66d-afcc-40fb-810b-16eca169aba6 · outbound

This paper cites Classification of chaotic time series with deep learning.

An Improved Autoencoder Conjugacy Network to Learn Chaotic Maps Classification of chaotic time series with deep learning

Reference 3

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Observation 6a11f448-4d3f-4775-b682-b4aa1d1e8f0d · outbound

This paper cites Long- term prediction of chaotic systems with machine learning.

An Improved Autoencoder Conjugacy Network to Learn Chaotic Maps Long- term prediction of chaotic systems with machine learning

Reference 4

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Observation edbc3e52-1698-4b37-91af-f95ee8a36feb · outbound

This paper cites Controlling nonlinear dynamical sys- tems into arbitrary states using machine learning.

An Improved Autoencoder Conjugacy Network to Learn Chaotic Maps Controlling nonlinear dynamical sys- tems into arbitrary states using machine learning

Reference 5

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Observation a27c169b-2adb-4e67-81f8-e24b5cb8dc7d · outbound

This paper cites Modeling of nonlinear system based on deep learning framework.

An Improved Autoencoder Conjugacy Network to Learn Chaotic Maps Modeling of nonlinear system based on deep learning framework

Reference 6

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Observation 34ec8bd3-3232-4405-906e-4c1a367fa364 · outbound

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

An Improved Autoencoder Conjugacy Network to Learn Chaotic Maps Deep learning for universal linear embeddings of nonlinear dynamics

Reference 7

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Observation bef365aa-a26c-4acd-b7e7-e36aff26522c · outbound

This paper cites Model- free prediction of large spatiotemporally chaotic systems from data: A reservoir computing approach.

An Improved Autoencoder Conjugacy Network to Learn Chaotic Maps Model- free prediction of large spatiotemporally chaotic systems from data: A reservoir computing approach

Reference 8

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This paper cites New results for prediction of chaotic systems using deep recurrent neural networks.

An Improved Autoencoder Conjugacy Network to Learn Chaotic Maps New results for prediction of chaotic systems using deep recurrent neural networks

Reference 9

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Observation f019f5a8-cd68-4db1-b3b2-bb16c61ef0ed · outbound

This paper cites Neural machine-based forecasting of chaotic dynamics.

An Improved Autoencoder Conjugacy Network to Learn Chaotic Maps Neural machine-based forecasting of chaotic dynamics

Reference 10

Resolution: verified fuzzy; raw_fallback, observed 2026-08-06T17:53:19.865017Z

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Observation 5aff02e4-127e-40ed-b084-2898d850ed67 · outbound

This paper cites Modeling chaotic sys- tems: Dynamical equations vs machine learning approach.

An Improved Autoencoder Conjugacy Network to Learn Chaotic Maps Modeling chaotic sys- tems: Dynamical equations vs machine learning approach

Reference 11

Resolution: verified fuzzy; raw_fallback, observed 2026-08-06T17:53:19.852615Z

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Observation a9724fa3-8685-41c7-bce3-5932ba8dad5f · outbound

This paper cites Physics-informed neu- ral networks: A deep learning framework for solving forward and inverse problems involving nonlinear partial differential equations.

An Improved Autoencoder Conjugacy Network to Learn Chaotic Maps Physics-informed neu- ral networks: A deep learning framework for solving forward and inverse problems involving nonlinear partial differential equations

Reference 12

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This paper cites Physica D, 2025.

An Improved Autoencoder Conjugacy Network to Learn Chaotic Maps Physica D, 2025

Reference 13

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Observation 0c094aea-f667-4a40-b10b-8986970586ac · outbound

This paper cites Linearly recurrent autoencoder networks for learning dynamics.

An Improved Autoencoder Conjugacy Network to Learn Chaotic Maps Linearly recurrent autoencoder networks for learning dynamics

Reference 14

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Observation f8aeeb17-f687-48d6-a46a-8f624a7a8473 · outbound

This paper cites Exploration and prediction of fluid dynamical systems using auto- encoder technology.

An Improved Autoencoder Conjugacy Network to Learn Chaotic Maps Exploration and prediction of fluid dynamical systems using auto- encoder technology

Reference 15

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Observation 08388453-6457-4d6e-a61c-276ae2f9e36f · outbound

This paper cites Deep neural networks for nonlinear model order reduction of unsteady flows.

An Improved Autoencoder Conjugacy Network to Learn Chaotic Maps Deep neural networks for nonlinear model order reduction of unsteady flows

Reference 16

Resolution: verified fuzzy; raw_fallback, observed 2026-08-06T17:53:19.802777Z

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Observation 3bc17641-bad7-4fd7-8487-8807fb80df1b · outbound

This paper cites Model reduction of dynamical systems on non- linear manifolds using deep convolutional autoencoders.

An Improved Autoencoder Conjugacy Network to Learn Chaotic Maps Model reduction of dynamical systems on non- linear manifolds using deep convolutional autoencoders

Reference 17

Resolution: verified fuzzy; raw_fallback, observed 2026-08-06T17:53:19.791873Z

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Observation c7770d5d-a667-482e-ae69-151db9a81b73 · outbound

This paper cites Multi-level convolutional autoencoder networks for parametric prediction of spatio-temporal dynamics.Computer Methods in Applied Mechanics and Engineering , 372:113379, 2020.

An Improved Autoencoder Conjugacy Network to Learn Chaotic Maps Multi-level convolutional autoencoder networks for parametric prediction of spatio-temporal dynamics.Computer Methods in Applied Mechanics and Engineering , 372:113379, 2020

Reference 18

Resolution: verified fuzzy; raw_fallback, observed 2026-08-06T17:53:19.780150Z

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This paper cites Machine learning approach to model order reduction of nonlinear systems via autoencoder and lstm networks.

An Improved Autoencoder Conjugacy Network to Learn Chaotic Maps Machine learning approach to model order reduction of nonlinear systems via autoencoder and lstm networks

Reference 19

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Observation a359851c-88d0-40b8-9738-8999888f8c6d · outbound

This paper cites Learning physics constrained dynamics using autoencoders.

An Improved Autoencoder Conjugacy Network to Learn Chaotic Maps Learning physics constrained dynamics using autoencoders

Reference 20

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Observation 7a4dccaa-53f1-4a42-b9fc-c2bb04c19600 · outbound

This paper cites Autoencoder neural net- works enable low dimensional structure analyses of microbial growth dynamics.

An Improved Autoencoder Conjugacy Network to Learn Chaotic Maps Autoencoder neural net- works enable low dimensional structure analyses of microbial growth dynamics

Reference 21

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Observation db4d8f53-b7bd-4332-af51-cdb7f651c2b6 · outbound

This paper cites Data- driven discovery of coordinates and governing equations.

An Improved Autoencoder Conjugacy Network to Learn Chaotic Maps Data- driven discovery of coordinates and governing equations

Reference 22

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Observation a91b9d61-3eb1-40b7-aeac-8ff246b4988b · outbound

This paper cites Deep learning of conjugate mappings.

An Improved Autoencoder Conjugacy Network to Learn Chaotic Maps Deep learning of conjugate mappings

Reference 23

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Observation 5890252c-6db2-4755-8d70-c51a91d9cb91 · outbound

This paper cites Relaxing conjugacy to fit modeling in dynamical systems.

An Improved Autoencoder Conjugacy Network to Learn Chaotic Maps Relaxing conjugacy to fit modeling in dynamical systems

Reference 24

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Observation bf2f4835-7e1b-439c-8003-77e0ad8b1ea3 · outbound

This paper cites A concept of homeomorphic defect for defin- ing mostly conjugate dynamical systems.

An Improved Autoencoder Conjugacy Network to Learn Chaotic Maps A concept of homeomorphic defect for defin- ing mostly conjugate dynamical systems

Reference 25

Resolution: verified fuzzy; raw_fallback, observed 2026-08-06T17:53:19.697146Z

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Observation aab883d8-fbe2-451a-881c-c1097ede8812 · outbound

This paper cites On comparing dynamical systems by defective conjugacy: A symbolic dynamics interpretation of commuter functions.

An Improved Autoencoder Conjugacy Network to Learn Chaotic Maps On comparing dynamical systems by defective conjugacy: A symbolic dynamics interpretation of commuter functions

Reference 26

Resolution: verified fuzzy; raw_fallback, observed 2026-08-06T17:53:19.685669Z

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Observation 153a6276-bb83-4a2e-8f1d-f4cab7324faa · outbound

This paper cites Theorem to generate independently and uniformly distributed chaotic key stream via topologically conjugated maps of tent map.

An Improved Autoencoder Conjugacy Network to Learn Chaotic Maps Theorem to generate independently and uniformly distributed chaotic key stream via topologically conjugated maps of tent map

Reference 27

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Observation aa899373-5669-487a-bfa8-ad6ebcbf1b04 · outbound

This paper cites Quadratic function chaotic system and its application on digital image encryption.

An Improved Autoencoder Conjugacy Network to Learn Chaotic Maps Quadratic function chaotic system and its application on digital image encryption

Reference 28

Resolution: verified fuzzy; raw_fallback, observed 2026-08-06T17:53:19.660430Z

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Observation 860ce7e4-58f4-4aca-8040-c42341f4456c · outbound

This paper cites Behavior of logistic map and some of its conjugate maps.

An Improved Autoencoder Conjugacy Network to Learn Chaotic Maps Behavior of logistic map and some of its conjugate maps

Reference 29

Resolution: verified fuzzy; raw_fallback, observed 2026-08-06T17:53:19.648038Z

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Observation c4209c61-4c95-4912-a29a-6cb8b267fb3e · outbound

This paper cites Differentiable conjugacies for one- dimensional maps.

An Improved Autoencoder Conjugacy Network to Learn Chaotic Maps Differentiable conjugacies for one- dimensional maps

Reference 30

Resolution: verified fuzzy; raw_fallback, observed 2026-08-06T17:53:19.636699Z

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This paper cites An introduction to dynamical systems and chaos, volume 1.

An Improved Autoencoder Conjugacy Network to Learn Chaotic Maps An introduction to dynamical systems and chaos, volume 1

Reference 31

Resolution: verified fuzzy; raw_fallback, observed 2026-08-06T17:53:19.624903Z

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Observation a03bd02c-a4fd-49c4-af93-eb793beb1aee · outbound

This paper cites Survival and weak chaos.

An Improved Autoencoder Conjugacy Network to Learn Chaotic Maps Survival and weak chaos

Reference 32

Resolution: verified fuzzy; raw_fallback, observed 2026-08-06T17:53:19.613969Z

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Observation b3ed3a64-a75f-494c-a8ba-a0ed4ca8356c · outbound

This paper cites Dropout: a simple way to prevent neural networks from overfitting.

An Improved Autoencoder Conjugacy Network to Learn Chaotic Maps Dropout: a simple way to prevent neural networks from overfitting

Reference 33

Resolution: unresolved; no resolver link, observed 2026-08-06T17:53:19.544737Z

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This paper cites Dropout as a bayesian approximation: Repre- senting model uncertainty in deep learning.

An Improved Autoencoder Conjugacy Network to Learn Chaotic Maps Dropout as a bayesian approximation: Repre- senting model uncertainty in deep learning

Reference 34

Resolution: verified fuzzy; raw_fallback, observed 2026-08-06T17:53:19.595506Z

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Observation 6a8deb91-4c2d-40a9-b7d3-b3a6f8cceace · outbound

This paper cites Simple and scalable predictive uncertainty estimation using deep ensembles.

An Improved Autoencoder Conjugacy Network to Learn Chaotic Maps Simple and scalable predictive uncertainty estimation using deep ensembles

Reference 35

Resolution: verified fuzzy; raw_fallback, observed 2026-08-06T17:53:19.584460Z

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Pith citing papers

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