Wasserstein Geometry of Information Loss in Nonlinear Dynamical Systems | Paper Citation Record

Observation d6df04dd-d382-450d-ad0a-bf0280957d56 · outbound

This paper cites Investigating observability properties from data in nonlinear dynamics.

Wasserstein Geometry of Information Loss in Nonlinear Dynamical Systems Investigating observability properties from data in nonlinear dynamics

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This paper cites Observability of multivariate differ- ential embeddings.Journal of Physics A: Mathematical and General, 38(28):6311, 2005.

Wasserstein Geometry of Information Loss in Nonlinear Dynamical Systems Observability of multivariate differ- ential embeddings.Journal of Physics A: Mathematical and General, 38(28):6311, 2005

Reference 2

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This paper cites Springer, 2005.

Wasserstein Geometry of Information Loss in Nonlinear Dynamical Systems Springer, 2005

Reference 3

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This paper cites Discov- ering governing equations from partial measurements with deep delay autoencoders.

Wasserstein Geometry of Information Loss in Nonlinear Dynamical Systems Discov- ering governing equations from partial measurements with deep delay autoencoders

Reference 4

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This paper cites Invariant measures in time-delay coordinates for unique dynamical system identification.Physical Review Letters, 135(16):167202, 2025.

Wasserstein Geometry of Information Loss in Nonlinear Dynamical Systems Invariant measures in time-delay coordinates for unique dynamical system identification.Physical Review Letters, 135(16):167202, 2025

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This paper cites Measure- theoretic time-delay embedding.Journal of Statistical Physics, 192(12):171, 2025.

Wasserstein Geometry of Information Loss in Nonlinear Dynamical Systems Measure- theoretic time-delay embedding.Journal of Statistical Physics, 192(12):171, 2025

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This paper cites Does observability affect proso- ciality? Proceedings of the Royal Society B: Biological Sciences, 285(1875):20180116, 2018.

Wasserstein Geometry of Information Loss in Nonlinear Dynamical Systems Does observability affect proso- ciality? Proceedings of the Royal Society B: Biological Sciences, 285(1875):20180116, 2018

Reference 7

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This paper cites Extracting qualitative dynamics from experimental data.

Wasserstein Geometry of Information Loss in Nonlinear Dynamical Systems Extracting qualitative dynamics from experimental data

Reference 8

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Observation 145b31c9-b4a8-44fa-b6f6-16c94f6d0398 · outbound

This paper cites Chaos as an intermittently forced linear system.Nature communications, 8(1):19, 2017.

Wasserstein Geometry of Information Loss in Nonlinear Dynamical Systems Chaos as an intermittently forced linear system.Nature communications, 8(1):19, 2017

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This paper cites Modern Koopman Theory for Dynamical Systems.

Wasserstein Geometry of Information Loss in Nonlinear Dynamical Systems Modern Koopman Theory for Dynamical Systems

Reference 10

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This paper cites Discovering governing equa- tions from data by sparse identification of nonlinear dynamical systems.Proceedings of the national academy of sciences, 113(15):3932–3937, 2016.

Wasserstein Geometry of Information Loss in Nonlinear Dynamical Systems Discovering governing equa- tions from data by sparse identification of nonlinear dynamical systems.Proceedings of the national academy of sciences, 113(15):3932–3937, 2016

Reference 11

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This paper cites Data- driven discovery of coordinates and governing equations.Proceedings of the National Academy of Sciences, 116(45):22445–22451, 2019.

Wasserstein Geometry of Information Loss in Nonlinear Dynamical Systems Data- driven discovery of coordinates and governing equations.Proceedings of the National Academy of Sciences, 116(45):22445–22451, 2019

Reference 12

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Observation cdf96031-b6c8-414a-b379-20c7b94f6c5a · outbound

This paper cites Differential embedding of the lorenz attractor.Physical Review E—Statistical, Nonlinear, and Soft Matter Physics, 81(6):066220, 2010.

Wasserstein Geometry of Information Loss in Nonlinear Dynamical Systems Differential embedding of the lorenz attractor.Physical Review E—Statistical, Nonlinear, and Soft Matter Physics, 81(6):066220, 2010

Reference 13

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Observation 1dc5302e-759b-46c3-9b80-533bce5be378 · outbound

This paper cites Delay-coordinate maps and the spectra of koopman operators.Journal of Statistical Physics, 175(6):1107–1145, 2019.

Wasserstein Geometry of Information Loss in Nonlinear Dynamical Systems Delay-coordinate maps and the spectra of koopman operators.Journal of Statistical Physics, 175(6):1107–1145, 2019

Reference 14

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This paper cites Causal Discovery in Symmetric Dynamic Systems with Convergent Cross Mapping.

Wasserstein Geometry of Information Loss in Nonlinear Dynamical Systems Causal Discovery in Symmetric Dynamic Systems with Convergent Cross Mapping

Reference 15

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This paper cites Ergodic theory of chaos and strange attractors.

Wasserstein Geometry of Information Loss in Nonlinear Dynamical Systems Ergodic theory of chaos and strange attractors

Reference 16

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This paper cites Causal inference from cross-sectional earth system data with geographical convergent cross mapping.nature communications, 14(1):5875, 2023.

Wasserstein Geometry of Information Loss in Nonlinear Dynamical Systems Causal inference from cross-sectional earth system data with geographical convergent cross mapping.nature communications, 14(1):5875, 2023

Reference 17

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Observation f81181de-cede-43ab-b629-b1e87495b563 · outbound

This paper cites Delay-coordinate maps, coherence, and approximate spectra of evolution operators.

Wasserstein Geometry of Information Loss in Nonlinear Dynamical Systems Delay-coordinate maps, coherence, and approximate spectra of evolution operators

Reference 18

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Observation 80fd1fa8-1205-4b67-9a19-53ceed6d8c31 · outbound

This paper cites Assessing observability of chaotic systems using delay differential analysis.

Wasserstein Geometry of Information Loss in Nonlinear Dynamical Systems Assessing observability of chaotic systems using delay differential analysis

Reference 19

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This paper cites Princeton university press, 2020.

Wasserstein Geometry of Information Loss in Nonlinear Dynamical Systems Princeton university press, 2020

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This paper cites Nonlinear controllability and observability.

Wasserstein Geometry of Information Loss in Nonlinear Dynamical Systems Nonlinear controllability and observability

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This paper cites Structured time-delay models for dynamical systems with connections to frenet–serret frame.

Wasserstein Geometry of Information Loss in Nonlinear Dynamical Systems Structured time-delay models for dynamical systems with connections to frenet–serret frame

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This paper cites Learning Discrepancy Models From Experimental Data.

Wasserstein Geometry of Information Loss in Nonlinear Dynamical Systems Learning Discrepancy Models From Experimental Data

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This paper cites Time-delay observables for koopman: Theory and applications.

Wasserstein Geometry of Information Loss in Nonlinear Dynamical Systems Time-delay observables for koopman: Theory and applications

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Observation b886d095-583e-4ec9-b762-1b2ac858da94 · outbound

This paper cites Determining embedding dimension for phase-space reconstruction using a geometrical construction.Physical review A, 45(6):3403, 1992.

Wasserstein Geometry of Information Loss in Nonlinear Dynamical Systems Determining embedding dimension for phase-space reconstruction using a geometrical construction.Physical review A, 45(6):3403, 1992

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Observation f70bfc9d-891c-4dbf-831f-5d5b5aa77612 · outbound

This paper cites Data-driven approximation of the koopman generator: Model reduction, system identification, and control.Physica D: Nonlinear Phenomena, 406:132416, 2020.

Wasserstein Geometry of Information Loss in Nonlinear Dynamical Systems Data-driven approximation of the koopman generator: Model reduction, system identification, and control.Physica D: Nonlinear Phenomena, 406:132416, 2020

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This paper cites Linear predictors for nonlinear dynamical systems: Global stability and control.Automatica, 93:149–160, 2018.

Wasserstein Geometry of Information Loss in Nonlinear Dynamical Systems Linear predictors for nonlinear dynamical systems: Global stability and control.Automatica, 93:149–160, 2018

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This paper cites Parsimony as the ultimate regularizer for physics-informed machine learning.Nonlinear Dynamics, 107(3):1801–1817, 2022.

Wasserstein Geometry of Information Loss in Nonlinear Dynamical Systems Parsimony as the ultimate regularizer for physics-informed machine learning.Nonlinear Dynamics, 107(3):1801–1817, 2022

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This paper cites Global modeling of the rössler system from the z-variable.Physics Letters A, 314(5-6):409–427, 2003.

Wasserstein Geometry of Information Loss in Nonlinear Dynamical Systems Global modeling of the rössler system from the z-variable.Physics Letters A, 314(5-6):409–427, 2003

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Wasserstein Geometry of Information Loss in Nonlinear Dynamical Systems Unresolved cited work

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This paper cites Relation between observability and differential embeddings for nonlinear dynamics.Physical Review E—Statistical, Nonlinear, and Soft Matter Physics, 71(6):066213, 2005.

Wasserstein Geometry of Information Loss in Nonlinear Dynamical Systems Relation between observability and differential embeddings for nonlinear dynamics.Physical Review E—Statistical, Nonlinear, and Soft Matter Physics, 71(6):066213, 2005

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Wasserstein Geometry of Information Loss in Nonlinear Dynamical Systems A symbolic network-based nonlinear theory for dynamical systems observ- ability

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This paper cites Control principles of complex systems.

Wasserstein Geometry of Information Loss in Nonlinear Dynamical Systems Control principles of complex systems

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Wasserstein Geometry of Information Loss in Nonlinear Dynamical Systems Controllability of complex networks.nature, 473(7346):167–173, 2011

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This paper cites Observability of complex systems.Proceedings of the National Academy of Sciences, 110(7):2460–2465, 2013.

Wasserstein Geometry of Information Loss in Nonlinear Dynamical Systems Observability of complex systems.Proceedings of the National Academy of Sciences, 110(7):2460–2465, 2013

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This paper cites Recurrent outbreaks of measles, chickenpox and mumps: I.

Wasserstein Geometry of Information Loss in Nonlinear Dynamical Systems Recurrent outbreaks of measles, chickenpox and mumps: I

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Wasserstein Geometry of Information Loss in Nonlinear Dynamical Systems Deep learning for universal linear embeddings of nonlinear dynamics.Nature Communications, 9(1):4950, 2018

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Wasserstein Geometry of Information Loss in Nonlinear Dynamical Systems Geometry from a time series.Physical review letters, 45(9):712, 1980

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Wasserstein Geometry of Information Loss in Nonlinear Dynamical Systems Observation of a strange attractor

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Wasserstein Geometry of Information Loss in Nonlinear Dynamical Systems Embedology.Journal of statistical Physics, 65(3):579–616, 1991

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Wasserstein Geometry of Information Loss in Nonlinear Dynamical Systems Do strange attractors govern ecological systems? BioScience, 35(6):342–350, 1985

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Wasserstein Geometry of Information Loss in Nonlinear Dynamical Systems Delay embeddings for forced systems

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This paper cites Broomhead, Mark E.

Wasserstein Geometry of Information Loss in Nonlinear Dynamical Systems Broomhead, Mark E

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Wasserstein Geometry of Information Loss in Nonlinear Dynamical Systems Consistent nonparametric regression.The annals of statistics, pages 595–620, 1977

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Wasserstein Geometry of Information Loss in Nonlinear Dynamical Systems Detecting causality in complex ecosystems.science, 338(6106):496–500, 2012

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Wasserstein Geometry of Information Loss in Nonlinear Dynamical Systems Nonlinear forecasting as a way of distinguishing chaos from measurement error in time series.Nature, 344(6268):734–741, 1990

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Wasserstein Geometry of Information Loss in Nonlinear Dynamical Systems Detecting strange attractors in turbulence

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Wasserstein Geometry of Information Loss in Nonlinear Dynamical Systems Spurious dimension from correlation algorithms applied to limited time-series data

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Wasserstein Geometry of Information Loss in Nonlinear Dynamical Systems A data–driven approximation of the koopman operator: Extending dynamic mode decomposition

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Wasserstein Geometry of Information Loss in Nonlinear Dynamical Systems Optimal transport for parameter identification of chaotic dynamics via invariant measures

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Wasserstein Geometry of Information Loss in Nonlinear Dynamical Systems Distinguishing time-delayed causal interactions using convergent cross mapping.Scientific reports, 5(1):14750, 2015

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