Learning dynamical systems from noisy data with Weak-form Kernel Ridge Regression | Paper Citation Record

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This paper cites Exploration and prediction of fluid dynamical systems using auto-encoder technology.Physics of Fluids, 32(6), 2020.

Learning dynamical systems from noisy data with Weak-form Kernel Ridge Regression Exploration and prediction of fluid dynamical systems using auto-encoder technology.Physics of Fluids, 32(6), 2020

Reference 1

Resolution: verified fuzzy; raw_fallback, observed 2026-07-05T21:11:29.411039Z

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This paper cites Kernel ridge regression hybrid method for wheat yield prediction with satellite-derived predictors.Remote Sensing, 14(5):1136, 2022.

Learning dynamical systems from noisy data with Weak-form Kernel Ridge Regression Kernel ridge regression hybrid method for wheat yield prediction with satellite-derived predictors.Remote Sensing, 14(5):1136, 2022

Reference 2

Resolution: verified fuzzy; raw_fallback, observed 2026-07-05T21:11:29.409012Z

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This paper cites Sampling procedures in function spaces and asymptotic equivalence with Shannon’ s sampling theory.Numerical functional analysis and optimization, 15(1-2):1–21, 1994.

Learning dynamical systems from noisy data with Weak-form Kernel Ridge Regression Sampling procedures in function spaces and asymptotic equivalence with Shannon’ s sampling theory.Numerical functional analysis and optimization, 15(1-2):1–21, 1994

Reference 3

Resolution: verified fuzzy; raw_fallback, observed 2026-07-05T21:11:29.451467Z

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This paper cites Complete ensemble empirical mode decomposition hybridized with random forest and kernel ridge regression model for monthly rainfall forecasts.

Learning dynamical systems from noisy data with Weak-form Kernel Ridge Regression Complete ensemble empirical mode decomposition hybridized with random forest and kernel ridge regression model for monthly rainfall forecasts

Reference 4

Resolution: verified fuzzy; raw_fallback, observed 2026-07-05T21:11:29.455470Z

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This paper cites Dynamic data-driven local traffic state estimation and prediction.Transportation Research Part C: Emerging Technologies, 34:89–107, 2013.

Learning dynamical systems from noisy data with Weak-form Kernel Ridge Regression Dynamic data-driven local traffic state estimation and prediction.Transportation Research Part C: Emerging Technologies, 34:89–107, 2013

Reference 5

Resolution: verified fuzzy; raw_fallback, observed 2026-07-05T21:11:29.464728Z

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This paper cites Data-driven analysis and forecasting of highway traffic dynamics.Nature communications, 11(1):2090, 2020.

Learning dynamical systems from noisy data with Weak-form Kernel Ridge Regression Data-driven analysis and forecasting of highway traffic dynamics.Nature communications, 11(1):2090, 2020

Reference 6

Resolution: verified fuzzy; raw_fallback, observed 2026-07-05T21:11:29.447641Z

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This paper cites Approximation error for quasi-interpolators and (multi-) wavelet expansions.

Learning dynamical systems from noisy data with Weak-form Kernel Ridge Regression Approximation error for quasi-interpolators and (multi-) wavelet expansions

Reference 7

Resolution: verified fuzzy; raw_fallback, observed 2026-07-05T21:11:29.376154Z

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This paper cites Weak form-based data-driven modeling: computationally efficient and noise robust equation learning and parameter inference.

Learning dynamical systems from noisy data with Weak-form Kernel Ridge Regression Weak form-based data-driven modeling: computationally efficient and noise robust equation learning and parameter inference

Reference 8

Resolution: verified fuzzy; raw_fallback, observed 2026-07-05T21:11:29.423202Z

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This paper cites John Wiley & Sons.

Learning dynamical systems from noisy data with Weak-form Kernel Ridge Regression John Wiley & Sons

Reference 9

Resolution: verified fuzzy; raw_fallback, observed 2026-07-05T21:11:29.337195Z

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Observation 8965524d-2ffd-45b0-848e-0481f83f71aa · outbound

This paper cites Predicting regime changes and durations in Lorenz’ s atmospheric convection model.Chaos: An Interdisciplinary Journal of Nonlinear Science, 30(10), 2020.

Learning dynamical systems from noisy data with Weak-form Kernel Ridge Regression Predicting regime changes and durations in Lorenz’ s atmospheric convection model.Chaos: An Interdisciplinary Journal of Nonlinear Science, 30(10), 2020

Reference 10

Resolution: verified fuzzy; raw_fallback, observed 2026-07-05T21:11:29.457469Z

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This paper cites Cambridge University Press.

Learning dynamical systems from noisy data with Weak-form Kernel Ridge Regression Cambridge University Press

Reference 11

Resolution: verified fuzzy; raw_fallback, observed 2026-07-05T21:11:29.327399Z

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

Learning dynamical systems from noisy data with Weak-form Kernel Ridge Regression Discovering governing equations from data by sparse identification of nonlinear dynamical systems.Proceedings of the national academy of sciences, 113(15):3932– 3937

Reference 12

Resolution: verified fuzzy; raw_fallback, observed 2026-07-05T21:11:29.329264Z

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Observation f23d3284-e518-4ab7-ad22-4bdbba885cb1 · outbound

This paper cites Sparse identification of nonlinear dynamics with control (SINDYc).IFAC-PapersOnLine, 49(18):710–715, 2016.

Learning dynamical systems from noisy data with Weak-form Kernel Ridge Regression Sparse identification of nonlinear dynamics with control (SINDYc).IFAC-PapersOnLine, 49(18):710–715, 2016

Reference 13

Resolution: verified fuzzy; raw_fallback, observed 2026-07-05T21:11:29.394548Z

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Observation 19ea7cd7-617c-419d-b2a4-b92e72bf7644 · outbound

This paper cites An intelligent system for financial time series prediction combining dynami- cal systems theory, fractal theory, and statistical methods.

Learning dynamical systems from noisy data with Weak-form Kernel Ridge Regression An intelligent system for financial time series prediction combining dynami- cal systems theory, fractal theory, and statistical methods

Reference 14

Resolution: verified fuzzy; raw_fallback, observed 2026-07-05T21:11:29.325782Z

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Observation 6c9e0fe8-51e4-470d-a112-9a127f64ce95 · outbound

This paper cites Neural ordinary differential equations.Advances in neural information processing systems, 31.

Learning dynamical systems from noisy data with Weak-form Kernel Ridge Regression Neural ordinary differential equations.Advances in neural information processing systems, 31

Reference 15

Resolution: verified fuzzy; raw_fallback, observed 2026-07-05T21:11:29.401562Z

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Observation 78a02c3b-0372-4335-a111-51106c9c94ef · outbound

This paper cites Machine learning with data assimilation and uncertainty quantification for dynamical systems: a review.IEEE/CAA Journal of Automatica Sinica, 10(6):1361–1387, 2023.

Learning dynamical systems from noisy data with Weak-form Kernel Ridge Regression Machine learning with data assimilation and uncertainty quantification for dynamical systems: a review.IEEE/CAA Journal of Automatica Sinica, 10(6):1361–1387, 2023

Reference 16

Resolution: verified fuzzy; raw_fallback, observed 2026-07-05T21:11:29.397178Z

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Observation f9f16d87-ebcf-4b00-b30d-3765c7bad753 · outbound

This paper cites From reliable weather forecasts to skilful climate response: A dynamical systems approach.Quarterly Journal of the Royal Meteorological Society, 145(720):1052–1069, 2019.

Learning dynamical systems from noisy data with Weak-form Kernel Ridge Regression From reliable weather forecasts to skilful climate response: A dynamical systems approach.Quarterly Journal of the Royal Meteorological Society, 145(720):1052–1069, 2019

Reference 17

Resolution: verified fuzzy; raw_fallback, observed 2026-07-05T21:11:29.390726Z

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Observation ef50d322-e1b6-489f-815b-a34bf9a5d2de · outbound

This paper cites Diffusion maps.Applied and computational harmonic analysis, 21(1):5–30.

Learning dynamical systems from noisy data with Weak-form Kernel Ridge Regression Diffusion maps.Applied and computational harmonic analysis, 21(1):5–30

Reference 18

Resolution: verified fuzzy; raw_fallback, observed 2026-07-05T21:11:29.339132Z

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This paper cites Graph Laplacian tomography from unknown random projections.IEEE Transactions on Image Processing, 17(10):1891–1899, 2008.

Learning dynamical systems from noisy data with Weak-form Kernel Ridge Regression Graph Laplacian tomography from unknown random projections.IEEE Transactions on Image Processing, 17(10):1891–1899, 2008

Reference 19

Resolution: verified fuzzy; raw_fallback, observed 2026-07-05T21:11:29.346748Z

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This paper cites The mpedmd algorithm for data-driven computations of measure-preserving dynamical systems.SIAM Journal on Numerical Analysis, 61(3):1585–1608, 2023.

Learning dynamical systems from noisy data with Weak-form Kernel Ridge Regression The mpedmd algorithm for data-driven computations of measure-preserving dynamical systems.SIAM Journal on Numerical Analysis, 61(3):1585–1608, 2023

Reference 20

Resolution: verified fuzzy; raw_fallback, observed 2026-07-05T21:11:29.333654Z

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This paper cites Residual dynamic mode decomposition: robust and verified Koopmanism.Journal of Fluid Mechanics, 955:A21, 2023.

Learning dynamical systems from noisy data with Weak-form Kernel Ridge Regression Residual dynamic mode decomposition: robust and verified Koopmanism.Journal of Fluid Mechanics, 955:A21, 2023

Reference 21

Resolution: verified fuzzy; raw_fallback, observed 2026-07-05T21:11:29.378149Z

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This paper cites Lyapunov exponents of the Kuramoto–Sivashinsky PDE.The ANZIAM Journal, 61(3):270–285, 2019.

Learning dynamical systems from noisy data with Weak-form Kernel Ridge Regression Lyapunov exponents of the Kuramoto–Sivashinsky PDE.The ANZIAM Journal, 61(3):270–285, 2019

Reference 22

Resolution: verified fuzzy; raw_fallback, observed 2026-07-05T21:11:29.335464Z

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Observation bea5b47d-c781-4962-b8c7-7eee397c1e4e · outbound

This paper cites Combining physics-based and data-driven modeling in well construction: Hybrid fluid dynamics modeling.Journal of Natural Gas Science and Engineering, 97:104348, 2022.

Learning dynamical systems from noisy data with Weak-form Kernel Ridge Regression Combining physics-based and data-driven modeling in well construction: Hybrid fluid dynamics modeling.Journal of Natural Gas Science and Engineering, 97:104348, 2022

Reference 23

Resolution: verified fuzzy; raw_fallback, observed 2026-07-05T21:11:29.382240Z

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Observation 76bd2fc1-d63d-4cdf-a489-cd3d1ff193f9 · outbound

This paper cites Nonlinear forecasting with many predictors using kernel ridge regression.International Journal of Forecasting, 32(3):736–753, 2016.

Learning dynamical systems from noisy data with Weak-form Kernel Ridge Regression Nonlinear forecasting with many predictors using kernel ridge regression.International Journal of Forecasting, 32(3):736–753, 2016

Reference 24

Resolution: verified fuzzy; raw_fallback, observed 2026-07-05T21:11:29.372228Z

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This paper cites Data-driven discovery of intrinsic dynamics.Nature Machine Intelli- gence, 4(12):1113–1120, 2022.

Learning dynamical systems from noisy data with Weak-form Kernel Ridge Regression Data-driven discovery of intrinsic dynamics.Nature Machine Intelli- gence, 4(12):1113–1120, 2022

Reference 25

Resolution: verified fuzzy; raw_fallback, observed 2026-07-05T21:11:29.369840Z

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This paper cites Next generation reservoir computing.

Learning dynamical systems from noisy data with Weak-form Kernel Ridge Regression Next generation reservoir computing

Reference 26

Resolution: verified fuzzy; raw_fallback, observed 2026-07-05T21:11:29.367526Z

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Observation 7edaf317-f59e-4531-80d2-1de958005a65 · outbound

This paper cites Data-driven prediction in dynamical systems: recent developments.

Learning dynamical systems from noisy data with Weak-form Kernel Ridge Regression Data-driven prediction in dynamical systems: recent developments

Reference 27

Resolution: verified fuzzy; raw_fallback, observed 2026-07-05T21:11:29.374188Z

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Observation c99bb05d-b8bc-4330-bea6-f3f74e0547b0 · outbound

This paper cites Learning dynamics from large biological data sets: machine learning meets systems biology.Current Opinion in Systems Biology, 22:1–7, 2020.

Learning dynamical systems from noisy data with Weak-form Kernel Ridge Regression Learning dynamics from large biological data sets: machine learning meets systems biology.Current Opinion in Systems Biology, 22:1–7, 2020

Reference 28

Resolution: verified fuzzy; raw_fallback, observed 2026-07-05T21:11:29.386714Z

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This paper cites PhD thesis, University of the Balearic Islands (UIB); Institute for Cross-Disciplinary Physics and Complex Systems, 2024.

Learning dynamical systems from noisy data with Weak-form Kernel Ridge Regression PhD thesis, University of the Balearic Islands (UIB); Institute for Cross-Disciplinary Physics and Complex Systems, 2024

Reference 29

Resolution: verified fuzzy; raw_fallback, observed 2026-07-05T21:11:29.323658Z

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Observation 47e765ec-3c51-44c6-a3e9-47d1fb346e8d · outbound

This paper cites Gaussian process priors with uncertain inputs application to multiple-step ahead time series forecasting.Advances in neural information processing systems, 15, 2002.

Learning dynamical systems from noisy data with Weak-form Kernel Ridge Regression Gaussian process priors with uncertain inputs application to multiple-step ahead time series forecasting.Advances in neural information processing systems, 15, 2002

Reference 30

Resolution: verified fuzzy; raw_fallback, observed 2026-07-05T21:11:29.399623Z

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Observation 4895b99a-500a-4d89-8153-123e7e848f0b · outbound

This paper cites an unresolved cited work.

Learning dynamical systems from noisy data with Weak-form Kernel Ridge Regression Unresolved cited work

Reference 31

Resolution: unresolved; raw_fallback, observed 2026-07-05T21:11:29.406281Z

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Observation ba67fa10-c00d-4cd5-b0e1-b6d2a5cf46b5 · outbound

This paper cites Supervised learning from noisy observations: Combining machine- learning techniques with data assimilation.Physica D: Nonlinear Phenomena, 423:132911, 2021.

Learning dynamical systems from noisy data with Weak-form Kernel Ridge Regression Supervised learning from noisy observations: Combining machine- learning techniques with data assimilation.Physica D: Nonlinear Phenomena, 423:132911, 2021

Reference 32

Resolution: verified fuzzy; raw_fallback, observed 2026-07-05T21:11:29.467046Z

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Observation e633fe41-2eb0-4cdd-90e8-a946d9a6ee87 · outbound

This paper cites Neural ODEs with Irregular and Noisy Data.

Learning dynamical systems from noisy data with Weak-form Kernel Ridge Regression Neural ODEs with Irregular and Noisy Data

Reference 33

Resolution: verified exact; arxiv_id, observed 2026-07-02T19:27:18.453749Z

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Observation f90437c9-6167-4712-a7d7-3df8fc612817 · outbound

This paper cites Diffusion maps kernel ridge regression.arXiv preprint, in preparation, 2026.

Learning dynamical systems from noisy data with Weak-form Kernel Ridge Regression Diffusion maps kernel ridge regression.arXiv preprint, in preparation, 2026

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Resolution: verified fuzzy; raw_fallback, observed 2026-07-05T21:11:29.468930Z

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Observation 866e789f-ce6e-453e-a559-c888f3093be1 · outbound

This paper cites Long short-term memory.Neural computation, 9(8):1735–1780.

Learning dynamical systems from noisy data with Weak-form Kernel Ridge Regression Long short-term memory.Neural computation, 9(8):1735–1780

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Resolution: verified fuzzy; raw_fallback, observed 2026-07-05T21:11:29.356772Z

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Observation 7cac1aec-0601-4401-a33d-1e952bb5ef6e · outbound

This paper cites Learning vector fields of differential equations on manifolds with geometrically constrained operator-valued kernels.

Learning dynamical systems from noisy data with Weak-form Kernel Ridge Regression Learning vector fields of differential equations on manifolds with geometrically constrained operator-valued kernels

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Resolution: verified fuzzy; raw_fallback, observed 2026-07-05T21:11:29.361222Z

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Observation af283600-9ec2-4710-b4fc-9c9691c7c693 · outbound

This paper cites A dynamic neural network architecture with immunology inspired optimization for weather data forecasting.Big data research, 14:81–92, 2018.

Learning dynamical systems from noisy data with Weak-form Kernel Ridge Regression A dynamic neural network architecture with immunology inspired optimization for weather data forecasting.Big data research, 14:81–92, 2018

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Resolution: verified fuzzy; raw_fallback, observed 2026-07-05T21:11:29.344909Z

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Observation 00d4bebd-ce9e-47f3-b394-b0a605874a6f · outbound

This paper cites SINDy-PI: a robust algorithm for parallel implicit sparse identification of nonlinear dynamics.Proceedings.

Learning dynamical systems from noisy data with Weak-form Kernel Ridge Regression SINDy-PI: a robust algorithm for parallel implicit sparse identification of nonlinear dynamics.Proceedings

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Resolution: verified fuzzy; raw_fallback, observed 2026-07-05T21:11:29.343069Z

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Observation 5ff5cd60-7c0a-46e8-9ba4-3529815efe47 · outbound

This paper cites A new approach to linear filtering and prediction problems.Transactions of the ASME–Journal of Basic Engineering, 1960.

Learning dynamical systems from noisy data with Weak-form Kernel Ridge Regression A new approach to linear filtering and prediction problems.Transactions of the ASME–Journal of Basic Engineering, 1960

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Resolution: verified fuzzy; raw_fallback, observed 2026-07-05T21:11:29.319911Z

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Observation de49be03-d00e-46b5-81dd-40d161ec428b · outbound

This paper cites Fourth-order time-stepping for stiff PDEs.SIAM Journal on Scientific Computing, 26(4):1214–1233, 2005.

Learning dynamical systems from noisy data with Weak-form Kernel Ridge Regression Fourth-order time-stepping for stiff PDEs.SIAM Journal on Scientific Computing, 26(4):1214–1233, 2005

Reference 40

Resolution: verified fuzzy; raw_fallback, observed 2026-07-05T21:11:29.420594Z

Source-reported events for the cited work

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Observation feb44958-097d-46c6-8948-f31c5247ed78 · outbound

This paper cites SIAM, 2016.

Learning dynamical systems from noisy data with Weak-form Kernel Ridge Regression SIAM, 2016

Reference 41

Resolution: verified fuzzy; raw_fallback, observed 2026-07-05T21:11:29.416836Z

Source-reported events for the cited work

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Observation b09fb3c3-bac1-42d8-a367-74508cc58127 · outbound

This paper cites The Lorenz system: hidden boundary of practical stability and the Lyapunov dimension.Nonlinear Dyn, 102:713–732, 2020.

Learning dynamical systems from noisy data with Weak-form Kernel Ridge Regression The Lorenz system: hidden boundary of practical stability and the Lyapunov dimension.Nonlinear Dyn, 102:713–732, 2020

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Resolution: verified fuzzy; raw_fallback, observed 2026-07-05T21:11:29.414849Z

Source-reported events for the cited work

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Observation cf28724e-c8d5-4086-8c78-98996419ff37 · outbound

This paper cites an unresolved cited work.

Learning dynamical systems from noisy data with Weak-form Kernel Ridge Regression Unresolved cited work

Reference 43

Resolution: unresolved; raw_fallback, observed 2026-07-05T21:11:29.412731Z

Source-reported events for the cited work

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Observation ab7cd0bc-3a61-4a17-a43b-caf47d17ef8a · outbound

This paper cites A Weak Penalty Neural ODE for Learning Chaotic Dynamics from Noisy Time Series.

Learning dynamical systems from noisy data with Weak-form Kernel Ridge Regression A Weak Penalty Neural ODE for Learning Chaotic Dynamics from Noisy Time Series

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Resolution: verified exact; local_arxiv, observed 2026-07-02T19:27:18.451200Z

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Observation 388f8b49-ce3c-4ef5-86b1-532752f12664 · outbound

This paper cites A survey on long short-term memory networks for time series prediction.Procedia Cirp, 99:650–655, 2021.

Learning dynamical systems from noisy data with Weak-form Kernel Ridge Regression A survey on long short-term memory networks for time series prediction.Procedia Cirp, 99:650–655, 2021

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Resolution: verified fuzzy; raw_fallback, observed 2026-07-05T21:11:29.430448Z

Source-reported events for the cited work

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Observation 51200d18-1505-4903-a362-fa053497aff6 · outbound

This paper cites On a measure of lack of fit in time series models.Biometrika, 65(2):297–303, 1978.

Learning dynamical systems from noisy data with Weak-form Kernel Ridge Regression On a measure of lack of fit in time series models.Biometrika, 65(2):297–303, 1978

Reference 46

Resolution: verified fuzzy; raw_fallback, observed 2026-07-05T21:11:29.341367Z

Source-reported events for the cited work

No event found in the named queried sources as of 2026-08-09T06:31:02.800959+00:00.

Observation 6bac3242-76bf-4c07-90a1-f20b312f5072 · outbound

This paper cites Hybridnet: integrating model-based and data-driven learning to predict evolution of dynamical systems.

Learning dynamical systems from noisy data with Weak-form Kernel Ridge Regression Hybridnet: integrating model-based and data-driven learning to predict evolution of dynamical systems

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Resolution: verified fuzzy; raw_fallback, observed 2026-07-05T21:11:29.418656Z

Source-reported events for the cited work

No event found in the named queried sources as of 2026-08-09T06:31:02.800959+00:00.

Observation 11270708-1206-423f-9d96-4e8eac41bce7 · outbound

This paper cites Deterministic nonperiodic flow 1.

Learning dynamical systems from noisy data with Weak-form Kernel Ridge Regression Deterministic nonperiodic flow 1

Reference 48

Resolution: verified fuzzy; raw_fallback, observed 2026-07-05T21:11:29.440115Z

Source-reported events for the cited work

No event found in the named queried sources as of 2026-08-09T06:31:02.800959+00:00.

Observation d66d3763-9687-49b4-977a-0915fbeb77d9 · outbound

This paper cites Ecological forecasting and data assimilation in a data-rich era.Ecological Applications, 21(5):1429– 1442, 2011.

Learning dynamical systems from noisy data with Weak-form Kernel Ridge Regression Ecological forecasting and data assimilation in a data-rich era.Ecological Applications, 21(5):1429– 1442, 2011

Reference 49

Resolution: verified fuzzy; raw_fallback, observed 2026-07-05T21:11:29.331204Z

Source-reported events for the cited work

No event found in the named queried sources as of 2026-08-09T06:31:02.800959+00:00.

Observation c2fd61b6-7a10-4057-ab81-fb90453c952d · outbound

This paper cites Academic press, 1982.

Learning dynamical systems from noisy data with Weak-form Kernel Ridge Regression Academic press, 1982

Reference 50

Resolution: verified fuzzy; raw_fallback, observed 2026-07-05T21:11:29.424927Z

Source-reported events for the cited work

No event found in the named queried sources as of 2026-08-09T06:31:02.800959+00:00.

Observation b7c4afa5-b5ae-4283-83f4-2afd84caa436 · outbound

This paper cites Weak SINDy for partial differential equations.Journal of Computational Physics, 443:110525, 2021.

Learning dynamical systems from noisy data with Weak-form Kernel Ridge Regression Weak SINDy for partial differential equations.Journal of Computational Physics, 443:110525, 2021

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Resolution: verified fuzzy; raw_fallback, observed 2026-07-05T21:11:29.317951Z

Source-reported events for the cited work

No event found in the named queried sources as of 2026-08-09T06:31:02.800959+00:00.

Observation 0f63ecfd-aa18-4829-bd85-7e88c7ebf8f5 · outbound

This paper cites Weak SINDy: Galerkin-based data-driven model selection.Multiscale Modeling & Simulation, 19(3):1474–1497, 2021.

Learning dynamical systems from noisy data with Weak-form Kernel Ridge Regression Weak SINDy: Galerkin-based data-driven model selection.Multiscale Modeling & Simulation, 19(3):1474–1497, 2021

Reference 52

Resolution: verified fuzzy; raw_fallback, observed 2026-07-05T21:11:29.380197Z

Source-reported events for the cited work

No event found in the named queried sources as of 2026-08-09T06:31:02.800959+00:00.

Observation 5a16cfd5-c1cd-4166-82a8-0ee4923753fe · outbound

This paper cites Asymptotic consistency of the WSINDy algorithm in the limit of continuum data.IMA Journal of Numerical Analysis, 45(6):3264–3312, 2025.

Learning dynamical systems from noisy data with Weak-form Kernel Ridge Regression Asymptotic consistency of the WSINDy algorithm in the limit of continuum data.IMA Journal of Numerical Analysis, 45(6):3264–3312, 2025

Reference 53

Resolution: verified fuzzy; raw_fallback, observed 2026-07-05T21:11:29.313562Z

Source-reported events for the cited work

No event found in the named queried sources as of 2026-08-09T06:31:02.800959+00:00.

Observation 22693dd6-9393-4d23-957d-26bcffb03500 · outbound

This paper cites The Weak Form Is Stronger Than You Think.

Learning dynamical systems from noisy data with Weak-form Kernel Ridge Regression The Weak Form Is Stronger Than You Think

Reference 54

Resolution: metadata mismatch; arxiv_id, observed 2026-07-02T19:27:18.448529Z

Source-reported events for the cited work

No event found in the named queried sources as of 2026-08-09T06:31:02.800959+00:00.

Observation ca18880f-b8d5-4e60-9387-299a7f4dc1ab · outbound

This paper cites On numerical approximations of the Koopman operator.Mathematics, 10(7):1180, 2022.

Learning dynamical systems from noisy data with Weak-form Kernel Ridge Regression On numerical approximations of the Koopman operator.Mathematics, 10(7):1180, 2022

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Resolution: verified fuzzy; raw_fallback, observed 2026-07-05T21:11:29.315587Z

Source-reported events for the cited work

No event found in the named queried sources as of 2026-08-09T06:31:02.800959+00:00.

Observation 7f0f1167-079f-4004-89ce-0cdf88b6c65a · outbound

This paper cites Springer.

Learning dynamical systems from noisy data with Weak-form Kernel Ridge Regression Springer

Reference 56

Resolution: verified fuzzy; raw_fallback, observed 2026-07-05T21:11:29.321775Z

Source-reported events for the cited work

No event found in the named queried sources as of 2026-08-09T06:31:02.800959+00:00.

Observation ec7c455e-3cc0-479d-9f6b-fc03f4b2c2a1 · outbound

This paper cites Data-driven methods for weather forecast.

Learning dynamical systems from noisy data with Weak-form Kernel Ridge Regression Data-driven methods for weather forecast

Reference 57

Resolution: verified fuzzy; raw_fallback, observed 2026-07-05T21:11:29.438279Z

Source-reported events for the cited work

No event found in the named queried sources as of 2026-08-09T06:31:02.800959+00:00.

Observation 902131b2-effb-4f94-a45a-54b804fce25c · outbound

This paper cites A review of data-driven discovery for dynamic systems.International Statistical Review, 91(3):464–492, 2023.

Learning dynamical systems from noisy data with Weak-form Kernel Ridge Regression A review of data-driven discovery for dynamic systems.International Statistical Review, 91(3):464–492, 2023

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Resolution: verified fuzzy; raw_fallback, observed 2026-07-05T21:11:29.365489Z

Source-reported events for the cited work

No event found in the named queried sources as of 2026-08-09T06:31:02.800959+00:00.

Observation 581e1322-c3f3-4535-90ce-6dcaa03f69d0 · outbound

This paper cites Comprehensive review of neural differential equations for time series analysis, 2025.

Learning dynamical systems from noisy data with Weak-form Kernel Ridge Regression Comprehensive review of neural differential equations for time series analysis, 2025

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Resolution: verified exact; arxiv_id, observed 2026-07-02T19:27:18.462063Z

Source-reported events for the cited work

No event found in the named queried sources as of 2026-08-09T06:31:02.800959+00:00.

Observation a36d97ab-bf78-48c4-9065-da628d8049b0 · outbound

This paper cites Data-driven discovery of dynamical models in biology.

Learning dynamical systems from noisy data with Weak-form Kernel Ridge Regression Data-driven discovery of dynamical models in biology

Reference 60

Resolution: verified exact; arxiv_id, observed 2026-07-03T02:17:06.411511Z

Source-reported events for the cited work

No event found in the named queried sources as of 2026-08-09T06:31:02.800959+00:00.

Observation 6846d1e0-13a6-43a6-88d7-f231d7bdefea · outbound

This paper cites Smoothing and differentiation of data by simplified least squares procedures.Analytical chemistry, 36(8):1627–1639.

Learning dynamical systems from noisy data with Weak-form Kernel Ridge Regression Smoothing and differentiation of data by simplified least squares procedures.Analytical chemistry, 36(8):1627–1639

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Resolution: verified fuzzy; raw_fallback, observed 2026-07-05T21:11:29.453616Z

Source-reported events for the cited work

No event found in the named queried sources as of 2026-08-09T06:31:02.800959+00:00.

Observation ae587b34-3d51-4638-990a-0f47d2b45e74 · outbound

This paper cites Data-driven reduced-complexity modeling of fluid flows: A community challenge.arXiv preprint arXiv:2601.06183, 2026.

Learning dynamical systems from noisy data with Weak-form Kernel Ridge Regression Data-driven reduced-complexity modeling of fluid flows: A community challenge.arXiv preprint arXiv:2601.06183, 2026

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Resolution: verified exact; arxiv_id, observed 2026-07-02T19:27:18.445927Z

Source-reported events for the cited work

No event found in the named queried sources as of 2026-08-09T06:31:02.800959+00:00.

Observation 11671967-e294-4e2d-a5a9-d2911bb6ed39 · outbound

This paper cites Communication in the presence of noise.Proceedings of the IRE, 37(1):10–21, 1949.

Learning dynamical systems from noisy data with Weak-form Kernel Ridge Regression Communication in the presence of noise.Proceedings of the IRE, 37(1):10–21, 1949

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Resolution: verified fuzzy; raw_fallback, observed 2026-07-05T21:11:29.432186Z

Source-reported events for the cited work

No event found in the named queried sources as of 2026-08-09T06:31:02.800959+00:00.

Observation 4c7534f5-8df3-4a62-958e-335f3434c46e · outbound

This paper cites Application of dynamic data driven application system in environmental science.Environmental Reviews, 22(3):287–297, 2014.

Learning dynamical systems from noisy data with Weak-form Kernel Ridge Regression Application of dynamic data driven application system in environmental science.Environmental Reviews, 22(3):287–297, 2014

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Resolution: verified fuzzy; raw_fallback, observed 2026-07-05T21:11:29.470982Z

Source-reported events for the cited work

No event found in the named queried sources as of 2026-08-09T06:31:02.800959+00:00.

Observation ecc7b448-c469-46c7-b729-59eb4a8d6ffc · outbound

This paper cites Learning solution operator of dynamical systems with diffusion maps kernel ridge regression.arXiv preprint arXiv:2512.17203, 2025.

Learning dynamical systems from noisy data with Weak-form Kernel Ridge Regression Learning solution operator of dynamical systems with diffusion maps kernel ridge regression.arXiv preprint arXiv:2512.17203, 2025

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Resolution: verified exact; arxiv_id, observed 2026-07-02T19:27:18.459301Z

Source-reported events for the cited work

No event found in the named queried sources as of 2026-08-09T06:31:02.800959+00:00.

Observation 40140258-3eb9-4a8f-958b-72fa9d584163 · outbound

This paper cites Recent advances in physical reservoir computing: A review.

Learning dynamical systems from noisy data with Weak-form Kernel Ridge Regression Recent advances in physical reservoir computing: A review

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Resolution: verified fuzzy; raw_fallback, observed 2026-07-05T21:11:29.384391Z

Source-reported events for the cited work

No event found in the named queried sources as of 2026-08-09T06:31:02.800959+00:00.

Observation 562b5d3e-3ec7-4e99-add3-104f680b0067 · outbound

This paper cites Sampling-50 years after Shannon.Proceedings of the IEEE, 88(4):569–587, 2002.

Learning dynamical systems from noisy data with Weak-form Kernel Ridge Regression Sampling-50 years after Shannon.Proceedings of the IEEE, 88(4):569–587, 2002

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Resolution: verified fuzzy; raw_fallback, observed 2026-07-05T21:11:29.443896Z

Source-reported events for the cited work

No event found in the named queried sources as of 2026-08-09T06:31:02.800959+00:00.

Observation ee60c7a7-0f65-42da-bf79-1995e7b3105e · outbound

This paper cites A general sampling theory for nonideal acquisition devices.IEEE Transactions on Signal Processing, 42(11):2915–2925, 2002.

Learning dynamical systems from noisy data with Weak-form Kernel Ridge Regression A general sampling theory for nonideal acquisition devices.IEEE Transactions on Signal Processing, 42(11):2915–2925, 2002

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Resolution: verified fuzzy; raw_fallback, observed 2026-07-05T21:11:29.445805Z

Source-reported events for the cited work

No event found in the named queried sources as of 2026-08-09T06:31:02.800959+00:00.

Observation ae239fd9-8247-4b70-b1ec-42869f012793 · outbound

This paper cites Polynomial spline signal approximations: filter design and asymptotic equivalence with Shannon’ s sampling theorem.IEEE Transactions on Information Theory, 38(1):95–103, 2002.

Learning dynamical systems from noisy data with Weak-form Kernel Ridge Regression Polynomial spline signal approximations: filter design and asymptotic equivalence with Shannon’ s sampling theorem.IEEE Transactions on Information Theory, 38(1):95–103, 2002

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Resolution: verified fuzzy; raw_fallback, observed 2026-07-05T21:11:29.442148Z

Source-reported events for the cited work

No event found in the named queried sources as of 2026-08-09T06:31:02.800959+00:00.

Observation 8f004345-1d13-4cd0-829a-64c025db766b · outbound

This paper cites A generalized sampling theory without band-limiting constraints.IEEE transactions on circuits and systems II: analog and digital signal processing, 45(8):959–969, 2002.

Learning dynamical systems from noisy data with Weak-form Kernel Ridge Regression A generalized sampling theory without band-limiting constraints.IEEE transactions on circuits and systems II: analog and digital signal processing, 45(8):959–969, 2002

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Resolution: verified fuzzy; raw_fallback, observed 2026-07-05T21:11:29.449651Z

Source-reported events for the cited work

No event found in the named queried sources as of 2026-08-09T06:31:02.800959+00:00.

Observation 6a829b1f-7c3d-48d6-b1a2-f6bae848fad5 · outbound

This paper cites an unresolved cited work.

Learning dynamical systems from noisy data with Weak-form Kernel Ridge Regression Unresolved cited work

Reference 71

Resolution: unresolved; raw_fallback, observed 2026-07-05T21:11:29.462786Z

Source-reported events for the cited work

No event found in the named queried sources as of 2026-08-09T06:31:02.800959+00:00.

Observation 8bd73046-f3a2-48ed-b392-6975169c11cf · outbound

This paper cites Kernel ridge regression.

Learning dynamical systems from noisy data with Weak-form Kernel Ridge Regression Kernel ridge regression

Reference 72

Resolution: verified fuzzy; raw_fallback, observed 2026-07-05T21:11:29.460829Z

Source-reported events for the cited work

No event found in the named queried sources as of 2026-08-09T06:31:02.800959+00:00.

Observation c2f2d233-87d2-41d4-9591-9f6bbdcf3269 · outbound

This paper cites Data-driven neural modeling and chaos control in fractional-order financial dynamical systems.AIP Advances, 16(1), 2026.

Learning dynamical systems from noisy data with Weak-form Kernel Ridge Regression Data-driven neural modeling and chaos control in fractional-order financial dynamical systems.AIP Advances, 16(1), 2026

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Resolution: verified fuzzy; raw_fallback, observed 2026-07-05T21:11:29.426773Z

Source-reported events for the cited work

No event found in the named queried sources as of 2026-08-09T06:31:02.800959+00:00.

Observation 70c9a6eb-5044-48f3-a7e5-5cbfc54401dc · outbound

This paper cites Gaussian process dynamical models.Advances in neural information processing systems, 18, 2005.

Learning dynamical systems from noisy data with Weak-form Kernel Ridge Regression Gaussian process dynamical models.Advances in neural information processing systems, 18, 2005

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Resolution: verified fuzzy; raw_fallback, observed 2026-07-05T21:11:29.428564Z

Source-reported events for the cited work

No event found in the named queried sources as of 2026-08-09T06:31:02.800959+00:00.

Observation 36a89f5a-0969-4544-86df-60a86a81ef61 · outbound

This paper cites A data–driven approximation of the Koopman operator: Extending dynamic mode decomposition.Journal of Nonlinear Science, 25(6):1307–1346, 2015.

Learning dynamical systems from noisy data with Weak-form Kernel Ridge Regression A data–driven approximation of the Koopman operator: Extending dynamic mode decomposition.Journal of Nonlinear Science, 25(6):1307–1346, 2015

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Resolution: verified fuzzy; raw_fallback, observed 2026-07-05T21:11:29.436204Z

Source-reported events for the cited work

No event found in the named queried sources as of 2026-08-09T06:31:02.800959+00:00.

Observation 20db2d85-eaa6-4943-8e44-2ffe28413645 · outbound

This paper cites Introduction to ‘communication in the presence of noise’ by CE Shannon.

Learning dynamical systems from noisy data with Weak-form Kernel Ridge Regression Introduction to ‘communication in the presence of noise’ by CE Shannon

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Resolution: verified fuzzy; raw_fallback, observed 2026-07-05T21:11:29.433990Z

Source-reported events for the cited work

No event found in the named queried sources as of 2026-08-09T06:31:02.800959+00:00.

Observation 564333ff-0946-496e-8b9c-37357006fc61 · outbound

This paper cites Reconstructing data-driven governing equations for cell phenotypic transitions: integration of data science and systems biology.Physical Biology, 19(6):061001, 2022.

Learning dynamical systems from noisy data with Weak-form Kernel Ridge Regression Reconstructing data-driven governing equations for cell phenotypic transitions: integration of data science and systems biology.Physical Biology, 19(6):061001, 2022

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Resolution: verified fuzzy; raw_fallback, observed 2026-07-05T21:11:29.459227Z

Source-reported events for the cited work

No event found in the named queried sources as of 2026-08-09T06:31:02.800959+00:00.

Observation 695da490-8869-436c-bb26-e9bec3cce794 · outbound

This paper cites Big data driven mobile traffic understanding and forecasting: A time series approach.IEEE transactions on services computing, 9(5):796–805.

Learning dynamical systems from noisy data with Weak-form Kernel Ridge Regression Big data driven mobile traffic understanding and forecasting: A time series approach.IEEE transactions on services computing, 9(5):796–805

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Resolution: verified fuzzy; raw_fallback, observed 2026-07-05T21:11:29.404225Z

Source-reported events for the cited work

No event found in the named queried sources as of 2026-08-09T06:31:02.800959+00:00.

Observation b0753c26-7ee5-4f2b-a892-3aa708b586c5 · outbound

This paper cites Emerging opportunities and challenges for the future of reservoir computing.Nature Communications, 15(1):2056, 2024.

Learning dynamical systems from noisy data with Weak-form Kernel Ridge Regression Emerging opportunities and challenges for the future of reservoir computing.Nature Communications, 15(1):2056, 2024

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Resolution: verified fuzzy; raw_fallback, observed 2026-07-05T21:11:29.350488Z

Source-reported events for the cited work

No event found in the named queried sources as of 2026-08-09T06:31:02.800959+00:00.

Observation 57506876-5398-4a0c-a576-0a45d0699066 · outbound

This paper cites Gaussian process for long-term time-series forecasting.

Learning dynamical systems from noisy data with Weak-form Kernel Ridge Regression Gaussian process for long-term time-series forecasting

Reference 80

Resolution: verified fuzzy; raw_fallback, observed 2026-07-05T21:11:29.348601Z

Source-reported events for the cited work

No event found in the named queried sources as of 2026-08-09T06:31:02.800959+00:00.

Observation e7c05632-87d5-4182-87ff-be6e396e1ed5 · outbound

This paper cites Inference of dynamic systems from noisy and sparse data via manifold-constrained gaussian processes.Proceedings of the National Academy of Sciences, 118(15):e2020397118, 2021.

Learning dynamical systems from noisy data with Weak-form Kernel Ridge Regression Inference of dynamic systems from noisy and sparse data via manifold-constrained gaussian processes.Proceedings of the National Academy of Sciences, 118(15):e2020397118, 2021

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Resolution: verified fuzzy; raw_fallback, observed 2026-07-05T21:11:29.352237Z

Source-reported events for the cited work

No event found in the named queried sources as of 2026-08-09T06:31:02.800959+00:00.

Observation 622affc6-9cf4-4f18-823a-998996b0006f · outbound

This paper cites Equation-free mechanistic ecosystem forecasting using empirical dynamic modeling.

Learning dynamical systems from noisy data with Weak-form Kernel Ridge Regression Equation-free mechanistic ecosystem forecasting using empirical dynamic modeling

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Resolution: verified fuzzy; raw_fallback, observed 2026-07-05T21:11:29.358855Z

Source-reported events for the cited work

No event found in the named queried sources as of 2026-08-09T06:31:02.800959+00:00.

Observation 210a0ae4-8977-462e-bdca-1e63433936d8 · outbound

This paper cites Learning networked dynamical system models with weak form and graph neural networks.Journal of Guidance Control and Dynamics, 2026.

Learning dynamical systems from noisy data with Weak-form Kernel Ridge Regression Learning networked dynamical system models with weak form and graph neural networks.Journal of Guidance Control and Dynamics, 2026

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Resolution: verified fuzzy; raw_fallback, observed 2026-07-05T21:11:29.363191Z

Source-reported events for the cited work

No event found in the named queried sources as of 2026-08-09T06:31:02.800959+00:00.

Observation 42440d2a-5092-40b0-8a5e-b2713090248a · outbound

This paper cites A review of recurrent neural networks: LSTM cells and network architectures.Neural computation, 31(7):1235–1270, 2019.

Learning dynamical systems from noisy data with Weak-form Kernel Ridge Regression A review of recurrent neural networks: LSTM cells and network architectures.Neural computation, 31(7):1235–1270, 2019

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Resolution: verified fuzzy; raw_fallback, observed 2026-07-05T21:11:29.354372Z

Source-reported events for the cited work

No event found in the named queried sources as of 2026-08-09T06:31:02.800959+00:00.

Observation e0ee9bbc-0077-46c2-be24-46178a3b4128 · outbound

This paper cites On the convergence of the SINDy algorithm.Multiscale Modeling & Simulation, 17(3):948–972, 2019.

Learning dynamical systems from noisy data with Weak-form Kernel Ridge Regression On the convergence of the SINDy algorithm.Multiscale Modeling & Simulation, 17(3):948–972, 2019

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Resolution: verified fuzzy; raw_fallback, observed 2026-07-05T21:11:29.388762Z

Source-reported events for the cited work

No event found in the named queried sources as of 2026-08-09T06:31:02.800959+00:00.

Observation ac608fb1-eec0-4045-a1d2-eed22aac20d0 · outbound

This paper cites Accelerating neural ODEs: a variational formulation-based approach.

Learning dynamical systems from noisy data with Weak-form Kernel Ridge Regression Accelerating neural ODEs: a variational formulation-based approach

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Resolution: verified fuzzy; raw_fallback, observed 2026-07-05T21:11:29.392602Z

Source-reported events for the cited work

No event found in the named queried sources as of 2026-08-09T06:31:02.800959+00:00.

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