Self-Supervised Evolution Operator Learning for High-Dimensional Dynamical Systems | Paper Citation Record

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This paper cites an unresolved cited work.

Self-Supervised Evolution Operator Learning for High-Dimensional Dynamical Systems Unresolved cited work

Reference 1

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Observation 485737b5-25a7-4400-b166-5bfc18a5f75b · outbound

This paper cites Lawrence Zitnick, and Zachary Ulissi.

Self-Supervised Evolution Operator Learning for High-Dimensional Dynamical Systems Lawrence Zitnick, and Zachary Ulissi

Reference 2

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Observation 11e823f6-6f6a-4470-8e3d-5738b425134a · outbound

This paper cites Acemd: accelerating biomolecular dynamics in the microsecond time scale.Journal of Chemical Theory and Computation, 5(6):1632–1639, 2009.

Self-Supervised Evolution Operator Learning for High-Dimensional Dynamical Systems Acemd: accelerating biomolecular dynamics in the microsecond time scale.Journal of Chemical Theory and Computation, 5(6):1632–1639, 2009

Reference 3

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Observation c72650e9-27b0-4e9e-ada4-2e3a7cf3b148 · outbound

This paper cites Smith, Berk Hess, and Erik Lindahl.

Self-Supervised Evolution Operator Learning for High-Dimensional Dynamical Systems Smith, Berk Hess, and Erik Lindahl

Reference 4

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Observation 34300f3b-5c83-4db9-83bb-64d8611cd620 · outbound

This paper cites Chodera, Robert T.

Self-Supervised Evolution Operator Learning for High-Dimensional Dynamical Systems Chodera, Robert T

Reference 5

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Observation bef9b3d1-69c9-4bbf-b413-8a13acb36008 · outbound

This paper cites The quiet revolution of numerical weather prediction.Nature, 525(7567):47–55, 2015.

Self-Supervised Evolution Operator Learning for High-Dimensional Dynamical Systems The quiet revolution of numerical weather prediction.Nature, 525(7567):47–55, 2015

Reference 6

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Observation 884c28fc-9bb0-42a4-baad-832a4c8c24b5 · outbound

This paper cites Cambridge University Press, 2014.

Self-Supervised Evolution Operator Learning for High-Dimensional Dynamical Systems Cambridge University Press, 2014

Reference 7

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Observation 55cbf194-e5f3-4898-93f1-848ced4654e4 · outbound

This paper cites FourCastNet: A Global Data-driven High-resolution Weather Model using Adaptive Fourier Neural Operators.

Self-Supervised Evolution Operator Learning for High-Dimensional Dynamical Systems FourCastNet: A Global Data-driven High-resolution Weather Model using Adaptive Fourier Neural Operators

Reference 8

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Observation 8287ba0c-88c0-49d8-850f-adf3629830e9 · outbound

This paper cites Pangu-Weather: A 3D High-Resolution Model for Fast and Accurate Global Weather Forecast.

Self-Supervised Evolution Operator Learning for High-Dimensional Dynamical Systems Pangu-Weather: A 3D High-Resolution Model for Fast and Accurate Global Weather Forecast

Reference 9

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Observation c06f3b92-074c-4367-8531-07ce835618ee · outbound

This paper cites Learning skillful medium-range global weather forecasting.Science, 382(6677):1416–1421, 2023.

Self-Supervised Evolution Operator Learning for High-Dimensional Dynamical Systems Learning skillful medium-range global weather forecasting.Science, 382(6677):1416–1421, 2023

Reference 10

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Observation 3af2d524-7a96-4a87-9187-e153d59a02d3 · outbound

This paper cites Neural general circulation models for weather and climate.Nature, 632(8027):1060–1066, 2024.

Self-Supervised Evolution Operator Learning for High-Dimensional Dynamical Systems Neural general circulation models for weather and climate.Nature, 632(8027):1060–1066, 2024

Reference 11

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Observation ea523abd-7f4b-42da-9fae-b84f2ff9d019 · outbound

This paper cites MIT Press, 1998.

Self-Supervised Evolution Operator Learning for High-Dimensional Dynamical Systems MIT Press, 1998

Reference 12

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Observation e51b2456-3a64-4b1f-8d2a-3f7a3df47a2d · outbound

This paper cites Mackey.Chaos, Fractals, and Noise, volume 97 ofApplied Mathematical Sciences.

Self-Supervised Evolution Operator Learning for High-Dimensional Dynamical Systems Mackey.Chaos, Fractals, and Noise, volume 97 ofApplied Mathematical Sciences

Reference 13

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Observation 6729aa44-71e3-456a-8213-26ed89d0a677 · outbound

This paper cites Cambridge University Press, April 2009.

Self-Supervised Evolution Operator Learning for High-Dimensional Dynamical Systems Cambridge University Press, April 2009

Reference 14

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Observation d16ae62c-9d89-4216-b25a-ce9811091fd4 · outbound

This paper cites Neural operator: Learning maps between function spaces with applications to pdes.Journal of Machine Learning Research, 24(89):1–97, 2023.

Self-Supervised Evolution Operator Learning for High-Dimensional Dynamical Systems Neural operator: Learning maps between function spaces with applications to pdes.Journal of Machine Learning Research, 24(89):1–97, 2023

Reference 15

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Observation 3f153f53-33c2-43c1-bf0c-57e0d5d63b12 · outbound

This paper cites Academic Press, 1972.

Self-Supervised Evolution Operator Learning for High-Dimensional Dynamical Systems Academic Press, 1972

Reference 16

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Observation be54f491-20c8-44c8-84d5-187301a2514f · outbound

This paper cites Molgedey and H.

Self-Supervised Evolution Operator Learning for High-Dimensional Dynamical Systems Molgedey and H

Reference 17

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Observation bb284872-3d6a-4c10-890d-7051f6fcf51a · outbound

This paper cites Springer, 2001.

Self-Supervised Evolution Operator Learning for High-Dimensional Dynamical Systems Springer, 2001

Reference 18

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Observation eeea4cda-21d1-4795-b775-847684acef0d · outbound

This paper cites Dynamic mode decomposition of numerical and experimental data.Journal of Fluid Mechanics, 656:5–28, 2010.

Self-Supervised Evolution Operator Learning for High-Dimensional Dynamical Systems Dynamic mode decomposition of numerical and experimental data.Journal of Fluid Mechanics, 656:5–28, 2010

Reference 19

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Self-Supervised Evolution Operator Learning for High-Dimensional Dynamical Systems Unresolved cited work

Reference 20

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This paper cites Identification of slow molecular order parameters for markov model construction.The Journal of Chemical Physics, 139(1), 2013.

Self-Supervised Evolution Operator Learning for High-Dimensional Dynamical Systems Identification of slow molecular order parameters for markov model construction.The Journal of Chemical Physics, 139(1), 2013

Reference 21

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Observation 58a697bf-7e6f-4e04-8fa1-51358eb81ab5 · outbound

This paper cites Deep learning the slow modes for rare events sampling.Proceedings of the National Academy of Sciences, 118(44):e2113533118, 2021.

Self-Supervised Evolution Operator Learning for High-Dimensional Dynamical Systems Deep learning the slow modes for rare events sampling.Proceedings of the National Academy of Sciences, 118(44):e2113533118, 2021

Reference 22

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Self-Supervised Evolution Operator Learning for High-Dimensional Dynamical Systems Unresolved cited work

Reference 23

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Observation 3a15403f-5363-40fa-bff9-f01adf657acc · outbound

This paper cites Nathan Kutz, Steven L.

Self-Supervised Evolution Operator Learning for High-Dimensional Dynamical Systems Nathan Kutz, Steven L

Reference 24

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Observation e58a3268-ba02-423e-a844-5e9fea98f1d4 · outbound

This paper cites On convergence of extended dynamic mode decomposition to the Koopman operator.Journal of Nonlinear Science, 28:687–710, 2018.

Self-Supervised Evolution Operator Learning for High-Dimensional Dynamical Systems On convergence of extended dynamic mode decomposition to the Koopman operator.Journal of Nonlinear Science, 28:687–710, 2018

Reference 25

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Observation 16977e04-4b2e-4220-b1f0-cbe10431b858 · outbound

This paper cites Learning dynamical systems via Koopman operator regression in reproducing kernel Hilbert spaces.Advances in Neural Information Processing Systems, 35:4017–4031, 2022.

Self-Supervised Evolution Operator Learning for High-Dimensional Dynamical Systems Learning dynamical systems via Koopman operator regression in reproducing kernel Hilbert spaces.Advances in Neural Information Processing Systems, 35:4017–4031, 2022

Reference 26

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Observation 07fba508-4eca-4b7f-bdb6-4a3af9587df6 · outbound

This paper cites Sharp spectral rates for Koopman operator learning.Advances in Neural Information Processing Systems, 36:32328–32339, 2023.

Self-Supervised Evolution Operator Learning for High-Dimensional Dynamical Systems Sharp spectral rates for Koopman operator learning.Advances in Neural Information Processing Systems, 36:32328–32339, 2023

Reference 27

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Observation 9d7a6146-0e98-40a2-9ae8-81442ce53747 · outbound

This paper cites Finite-data error bounds for Koopman-based prediction and control.Journal of Nonlinear Science, 33(1):14, 2023.

Self-Supervised Evolution Operator Learning for High-Dimensional Dynamical Systems Finite-data error bounds for Koopman-based prediction and control.Journal of Nonlinear Science, 33(1):14, 2023

Reference 28

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Observation 44bfbd5f-7343-47da-ba08-12079817299c · outbound

This paper cites A Kernel-Based Approach to Data-Driven Koopman Spectral Analysis.

Self-Supervised Evolution Operator Learning for High-Dimensional Dynamical Systems A Kernel-Based Approach to Data-Driven Koopman Spectral Analysis

Reference 29

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Observation c7f026e9-3631-4461-bc08-60b26a2e6038 · outbound

This paper cites Dynamic Mode Decomposition with Reproducing Kernels for Koopman Spectral Analysis.

Self-Supervised Evolution Operator Learning for High-Dimensional Dynamical Systems Dynamic Mode Decomposition with Reproducing Kernels for Koopman Spectral Analysis

Reference 30

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Observation ca2fb998-3516-4d34-8a69-c0ec4eede4d6 · outbound

This paper cites Eigendecompositions of transfer operators in reproducing kernel Hilbert spaces.Journal of Nonlinear Science, 30(1):283–315, 2019.

Self-Supervised Evolution Operator Learning for High-Dimensional Dynamical Systems Eigendecompositions of transfer operators in reproducing kernel Hilbert spaces.Journal of Nonlinear Science, 30(1):283–315, 2019

Reference 31

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Observation fb8eb136-4404-4a76-be44-10e72ba53dab · outbound

This paper cites Koopman spectra in reproducing kernel Hilbert spaces.Applied and Computational Harmonic Analysis, 49(2):573–607, 2020.

Self-Supervised Evolution Operator Learning for High-Dimensional Dynamical Systems Koopman spectra in reproducing kernel Hilbert spaces.Applied and Computational Harmonic Analysis, 49(2):573–607, 2020

Reference 32

Resolution: verified fuzzy; raw_fallback, observed 2026-08-07T14:36:28.167346Z

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Observation 73ef8e6a-35a4-490d-88bc-1a451c8a61cb · outbound

This paper cites Operator-theoretic framework for forecasting nonlinear time series with kernel analog techniques.Physica D: Nonlinear Phenomena, 409:132520, 2020.

Self-Supervised Evolution Operator Learning for High-Dimensional Dynamical Systems Operator-theoretic framework for forecasting nonlinear time series with kernel analog techniques.Physica D: Nonlinear Phenomena, 409:132520, 2020

Reference 33

Resolution: verified fuzzy; raw_fallback, observed 2026-08-07T14:36:28.069067Z

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Observation 65ee888e-e065-4afd-9e4c-a86458b8a78f · outbound

This paper cites Estimating koopman operators with sketching to provably learn large scale dynamical systems.

Self-Supervised Evolution Operator Learning for High-Dimensional Dynamical Systems Estimating koopman operators with sketching to provably learn large scale dynamical systems

Reference 34

Resolution: verified fuzzy; raw_fallback, observed 2026-08-07T14:36:27.941932Z

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Observation da601ea0-86bb-4760-b2c1-f8ab71c23e3d · outbound

This paper cites Deep learning for universal linear embeddings of nonlinear dynamics.Nature Communications, 9(1):4950, 2018.

Self-Supervised Evolution Operator Learning for High-Dimensional Dynamical Systems Deep learning for universal linear embeddings of nonlinear dynamics.Nature Communications, 9(1):4950, 2018

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Resolution: verified fuzzy; raw_fallback, observed 2026-08-07T14:36:27.789611Z

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Observation 6c27af98-d63b-4b80-9e22-b817b8f39945 · outbound

This paper cites Forecasting sequential data using consistent koopman autoencoders.

Self-Supervised Evolution Operator Learning for High-Dimensional Dynamical Systems Forecasting sequential data using consistent koopman autoencoders

Reference 36

Resolution: verified fuzzy; raw_fallback, observed 2026-08-07T14:36:27.682568Z

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Observation 2953e0b4-d2bd-4cf1-a656-8fcd3d5938ec · outbound

This paper cites Time-lagged autoencoders: Deep learning of slow collective variables for molecular kinetics.The Journal of Chemical Physics, 148(24), 2018.

Self-Supervised Evolution Operator Learning for High-Dimensional Dynamical Systems Time-lagged autoencoders: Deep learning of slow collective variables for molecular kinetics.The Journal of Chemical Physics, 148(24), 2018

Reference 37

Resolution: verified fuzzy; raw_fallback, observed 2026-08-07T14:36:27.502222Z

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Observation 15115712-fa43-4a5f-aa7a-1b21ba4e52b2 · outbound

This paper cites Neural Koopman prior for data assimilation.IEEE Transactions on Signal Processing, 2024.

Self-Supervised Evolution Operator Learning for High-Dimensional Dynamical Systems Neural Koopman prior for data assimilation.IEEE Transactions on Signal Processing, 2024

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Resolution: verified fuzzy; raw_fallback, observed 2026-08-07T14:36:27.354853Z

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Observation 247580cd-5781-421c-80bf-f275395386c4 · outbound

This paper cites Vampnets for deep learning of molecular kinetics.Nature communications, 9(1):5, 2018.

Self-Supervised Evolution Operator Learning for High-Dimensional Dynamical Systems Vampnets for deep learning of molecular kinetics.Nature communications, 9(1):5, 2018

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Resolution: unresolved; no resolver link, observed 2026-08-07T14:36:12.536954Z

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Observation 0e60af27-cf8b-46a9-b303-5c78030ebb65 · outbound

This paper cites Learning invariant representations of time-homogeneous stochastic dynamical systems.

Self-Supervised Evolution Operator Learning for High-Dimensional Dynamical Systems Learning invariant representations of time-homogeneous stochastic dynamical systems

Reference 40

Resolution: verified exact; local_arxiv, observed 2026-08-07T14:36:19.664295Z

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Observation 8831449e-066e-4784-aedc-04594733c461 · outbound

This paper cites Latent Representation and Simulation of Markov Processes via Time-Lagged Information Bottleneck.

Self-Supervised Evolution Operator Learning for High-Dimensional Dynamical Systems Latent Representation and Simulation of Markov Processes via Time-Lagged Information Bottleneck

Reference 41

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Observation 8d6f3802-9a6b-4290-9b0b-c5508ffd02b8 · outbound

This paper cites Task-oriented Koopman-based control with contrastive encoder.

Self-Supervised Evolution Operator Learning for High-Dimensional Dynamical Systems Task-oriented Koopman-based control with contrastive encoder

Reference 42

Resolution: verified fuzzy; raw_fallback, observed 2026-08-07T14:36:27.220290Z

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Observation 12076b5b-a367-4d47-a1dc-78d24df1c461 · outbound

This paper cites Data-Efficient Reinforcement Learning with Self-Predictive Representations.

Self-Supervised Evolution Operator Learning for High-Dimensional Dynamical Systems Data-Efficient Reinforcement Learning with Self-Predictive Representations

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Observation ebe5818b-d3e3-49da-8696-c2581a9da03f · outbound

This paper cites Learning mesh-based simulation with graph networks.

Self-Supervised Evolution Operator Learning for High-Dimensional Dynamical Systems Learning mesh-based simulation with graph networks

Reference 44

Resolution: verified fuzzy; raw_fallback, observed 2026-08-07T14:36:27.086844Z

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Observation 7163bbd0-6eaa-43cf-ae9e-9c4fd5a9cb82 · outbound

This paper cites Learning to simulate complex physics with graph networks.

Self-Supervised Evolution Operator Learning for High-Dimensional Dynamical Systems Learning to simulate complex physics with graph networks

Reference 45

Resolution: verified fuzzy; raw_fallback, observed 2026-08-07T14:36:26.945869Z

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Observation 20f3bfbe-4a1c-4a55-b418-61d3e7d6d25d · outbound

This paper cites Fourier Neural Operator for Parametric Partial Differential Equations.

Self-Supervised Evolution Operator Learning for High-Dimensional Dynamical Systems Fourier Neural Operator for Parametric Partial Differential Equations

Reference 46

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Observation eca4dd82-50c8-4b9c-afb0-e241fa65ee65 · outbound

This paper cites Koopman-Assisted Reinforcement Learning.

Self-Supervised Evolution Operator Learning for High-Dimensional Dynamical Systems Koopman-Assisted Reinforcement Learning

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Observation 07dcde50-9c3f-4406-9483-5d3aac2ec2ef · outbound

This paper cites Operator World Models for Reinforcement Learning.

Self-Supervised Evolution Operator Learning for High-Dimensional Dynamical Systems Operator World Models for Reinforcement Learning

Reference 48

Resolution: verified exact; local_arxiv, observed 2026-08-07T14:36:19.460140Z

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Observation b4c915fd-fbee-4e95-8567-f1e269564270 · outbound

This paper cites Representation Learning with Contrastive Predictive Coding.

Self-Supervised Evolution Operator Learning for High-Dimensional Dynamical Systems Representation Learning with Contrastive Predictive Coding

Reference 49

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Observation 9767d2be-906c-49fc-ad6d-1c4cf952b7a1 · outbound

This paper cites On mutual information maximization for representation learning.

Self-Supervised Evolution Operator Learning for High-Dimensional Dynamical Systems On mutual information maximization for representation learning

Reference 50

Resolution: verified fuzzy; raw_fallback, observed 2026-08-07T14:36:26.764941Z

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Observation e7a8afa2-8f53-4ae4-b957-c55244da8da2 · outbound

This paper cites Noise-contrastive estimation: A new estimation principle for unnormalized statistical models.

Self-Supervised Evolution Operator Learning for High-Dimensional Dynamical Systems Noise-contrastive estimation: A new estimation principle for unnormalized statistical models

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Resolution: verified fuzzy; raw_fallback, observed 2026-08-07T14:36:26.584573Z

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Observation 5ac336c7-9a99-4d17-b9aa-c16eb189d19b · outbound

This paper cites On vari- ational bounds of mutual information.

Self-Supervised Evolution Operator Learning for High-Dimensional Dynamical Systems On vari- ational bounds of mutual information

Reference 52

Resolution: verified fuzzy; raw_fallback, observed 2026-08-07T14:36:26.435584Z

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Observation fd9302ca-6d6f-43bc-aed8-fdb3c1447e7b · outbound

This paper cites Spectral Representation Learning for Conditional Moment Models.

Self-Supervised Evolution Operator Learning for High-Dimensional Dynamical Systems Spectral Representation Learning for Conditional Moment Models

Reference 53

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Observation 7ff298ec-c226-46d2-9bf1-8172f20460da · outbound

This paper cites Neural conditional probability for uncertainty quantification.Advances in Neural Information Processing Systems, 37:60999–61039, 2024.

Self-Supervised Evolution Operator Learning for High-Dimensional Dynamical Systems Neural conditional probability for uncertainty quantification.Advances in Neural Information Processing Systems, 37:60999–61039, 2024

Reference 54

Resolution: verified fuzzy; raw_fallback, observed 2026-08-07T14:36:26.272987Z

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Observation fee3ed55-2765-4736-b6a1-7eea9410e060 · outbound

This paper cites A class of statistics with asymptotically normal distribution.Breakthroughs in Statistics: Foundations and Basic Theory, pages 308–334, 1992.

Self-Supervised Evolution Operator Learning for High-Dimensional Dynamical Systems A class of statistics with asymptotically normal distribution.Breakthroughs in Statistics: Foundations and Basic Theory, pages 308–334, 1992

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Resolution: verified fuzzy; raw_fallback, observed 2026-08-07T14:36:26.139952Z

Source-reported events for the cited work

No event found in the named queried sources as of 2026-08-20T06:33:59.587034+00:00.

Observation 81eacfb8-1dfa-437b-af17-a5b655024825 · outbound

This paper cites Provable guarantees for self- supervised deep learning with spectral contrastive loss.Advances in neural information processing systems, 34:5000–5011, 2021.

Self-Supervised Evolution Operator Learning for High-Dimensional Dynamical Systems Provable guarantees for self- supervised deep learning with spectral contrastive loss.Advances in neural information processing systems, 34:5000–5011, 2021

Reference 56

Resolution: verified fuzzy; raw_fallback, observed 2026-08-07T14:36:25.987952Z

Source-reported events for the cited work

No event found in the named queried sources as of 2026-08-20T06:33:59.587034+00:00.

Observation c8bf45dd-4d82-48e6-ab01-0761ac9cb59c · outbound

This paper cites an unresolved cited work.

Self-Supervised Evolution Operator Learning for High-Dimensional Dynamical Systems Unresolved cited work

Reference 57

Resolution: unresolved; raw_fallback, observed 2026-08-07T14:36:25.837715Z

Source-reported events for the cited work

No event found in the named queried sources as of 2026-08-20T06:33:59.587034+00:00.

Observation 98a9d2b4-8fc7-4295-a270-9187317d309a · outbound

This paper cites Spectral Decomposition Representation for Reinforcement Learning.

Self-Supervised Evolution Operator Learning for High-Dimensional Dynamical Systems Spectral Decomposition Representation for Reinforcement Learning

Reference 58

Resolution: unresolved; no resolver link, observed 2026-08-07T14:36:14.010039Z

Source-reported events for the cited work

Unavailable: canonical work link unavailable.

Observation b620aabd-4ce8-4373-88fa-88eacb2e0ac6 · outbound

This paper cites Spectral Representation for Causal Estimation with Hidden Confounders.

Self-Supervised Evolution Operator Learning for High-Dimensional Dynamical Systems Spectral Representation for Causal Estimation with Hidden Confounders

Reference 59

Resolution: verified exact; local_arxiv, observed 2026-08-07T14:36:19.257407Z

Source-reported events for the cited work

No event found in the named queried sources as of 2026-08-20T06:33:59.587034+00:00.

Observation e0e87d0e-2e7f-4675-bd16-2b9661437c51 · outbound

This paper cites $f$-MICL: Understanding and Generalizing InfoNCE-based Contrastive Learning.

Self-Supervised Evolution Operator Learning for High-Dimensional Dynamical Systems $f$-MICL: Understanding and Generalizing InfoNCE-based Contrastive Learning

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Resolution: verified exact; local_arxiv, observed 2026-08-07T14:36:19.060279Z

Source-reported events for the cited work

No event found in the named queried sources as of 2026-08-20T06:33:59.587034+00:00.

Observation 6ea1dfa5-6f29-4c95-b500-49f12b899b34 · outbound

This paper cites Variational approach for learning markov processes from time series data.Journal of Nonlinear Science, 30(1):23–66, 2020.

Self-Supervised Evolution Operator Learning for High-Dimensional Dynamical Systems Variational approach for learning markov processes from time series data.Journal of Nonlinear Science, 30(1):23–66, 2020

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Resolution: verified fuzzy; raw_fallback, observed 2026-08-07T14:36:25.679703Z

Source-reported events for the cited work

No event found in the named queried sources as of 2026-08-20T06:33:59.587034+00:00.

Observation 003d3e98-3640-4cda-b83c-b8616111c636 · outbound

This paper cites an unresolved cited work.

Self-Supervised Evolution Operator Learning for High-Dimensional Dynamical Systems Unresolved cited work

Reference 62

Resolution: unresolved; raw_fallback, observed 2026-08-07T14:36:25.551467Z

Source-reported events for the cited work

No event found in the named queried sources as of 2026-08-20T06:33:59.587034+00:00.

Observation efa2657e-0807-4efe-aa4e-261672c86421 · outbound

This paper cites A simple framework for contrastive learning of visual representations.

Self-Supervised Evolution Operator Learning for High-Dimensional Dynamical Systems A simple framework for contrastive learning of visual representations

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Resolution: verified fuzzy; raw_fallback, observed 2026-08-07T14:36:25.417141Z

Source-reported events for the cited work

No event found in the named queried sources as of 2026-08-20T06:33:59.587034+00:00.

Observation 30c70d24-d6df-4cdb-8de8-2c24e5c78553 · outbound

This paper cites Bootstrap your own latent-a new approach to self-supervised learning.Advances in Neural Information Processing Systems, 33:21271–21284, 2020.

Self-Supervised Evolution Operator Learning for High-Dimensional Dynamical Systems Bootstrap your own latent-a new approach to self-supervised learning.Advances in Neural Information Processing Systems, 33:21271–21284, 2020

Reference 64

Resolution: verified fuzzy; raw_fallback, observed 2026-08-07T14:36:25.254594Z

Source-reported events for the cited work

No event found in the named queried sources as of 2026-08-20T06:33:59.587034+00:00.

Observation 6cace063-fcfa-4abb-8613-be984a33297e · outbound

This paper cites Barlow twins: Self- supervised learning via redundancy reduction.

Self-Supervised Evolution Operator Learning for High-Dimensional Dynamical Systems Barlow twins: Self- supervised learning via redundancy reduction

Reference 65

Resolution: verified fuzzy; raw_fallback, observed 2026-08-07T14:36:25.084950Z

Source-reported events for the cited work

No event found in the named queried sources as of 2026-08-20T06:33:59.587034+00:00.

Observation 428741b7-1630-4e07-885d-2937f3f690fe · outbound

This paper cites Exploring simple siamese representation learning.

Self-Supervised Evolution Operator Learning for High-Dimensional Dynamical Systems Exploring simple siamese representation learning

Reference 66

Resolution: verified fuzzy; raw_fallback, observed 2026-08-07T14:36:24.949639Z

Source-reported events for the cited work

No event found in the named queried sources as of 2026-08-20T06:33:59.587034+00:00.

Observation a6c9b1f9-731a-4ae2-b501-b38da300a767 · outbound

This paper cites Simplicial Embeddings in Self-Supervised Learning and Downstream Classification.

Self-Supervised Evolution Operator Learning for High-Dimensional Dynamical Systems Simplicial Embeddings in Self-Supervised Learning and Downstream Classification

Reference 67

Resolution: unresolved; no resolver link, observed 2026-08-07T14:36:14.728301Z

Source-reported events for the cited work

Unavailable: canonical work link unavailable.

Observation 6bfabcde-6dd1-43bd-b610-d365017dda2f · outbound

This paper cites Deterministic nonperiodic flow.Journal of Atmospheric Sciences, 20(2):130– 148, 1963.

Self-Supervised Evolution Operator Learning for High-Dimensional Dynamical Systems Deterministic nonperiodic flow.Journal of Atmospheric Sciences, 20(2):130– 148, 1963

Reference 68

Resolution: verified fuzzy; raw_fallback, observed 2026-08-07T14:36:24.777543Z

Source-reported events for the cited work

No event found in the named queried sources as of 2026-08-20T06:33:59.587034+00:00.

Observation b7493d2b-21eb-4933-9b11-45b9203f16c4 · outbound

This paper cites How fast-folding proteins fold.Science, 334(6055):517–520, 2011.

Self-Supervised Evolution Operator Learning for High-Dimensional Dynamical Systems How fast-folding proteins fold.Science, 334(6055):517–520, 2011

Reference 69

Resolution: verified fuzzy; raw_fallback, observed 2026-08-07T14:36:24.615555Z

Source-reported events for the cited work

No event found in the named queried sources as of 2026-08-20T06:33:59.587034+00:00.

Observation 5cd67e2d-6c0b-4ecd-b070-67a880a23983 · outbound

This paper cites an unresolved cited work.

Self-Supervised Evolution Operator Learning for High-Dimensional Dynamical Systems Unresolved cited work

Reference 70

Resolution: unresolved; raw_fallback, observed 2026-08-07T14:36:24.470386Z

Source-reported events for the cited work

No event found in the named queried sources as of 2026-08-20T06:33:59.587034+00:00.

Observation 313e055f-04a7-4bae-9d3b-f3edaf6c3760 · outbound

This paper cites Graphvampnet, using graph neural networks and variational approach to markov processes for dynamical modeling of biomolecules.The Journal of Chemical Physics, 156(18), 2022.

Self-Supervised Evolution Operator Learning for High-Dimensional Dynamical Systems Graphvampnet, using graph neural networks and variational approach to markov processes for dynamical modeling of biomolecules.The Journal of Chemical Physics, 156(18), 2022

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Resolution: verified fuzzy; raw_fallback, observed 2026-08-07T14:36:24.357794Z

Source-reported events for the cited work

No event found in the named queried sources as of 2026-08-20T06:33:59.587034+00:00.

Observation 42421430-d455-4a9a-842a-cf5c86d476dd · outbound

This paper cites Schnet: A continuous-filter convolutional neural network for modeling quantum interactions.Advances in Neural Information Processing Systems, 30, 2017.

Self-Supervised Evolution Operator Learning for High-Dimensional Dynamical Systems Schnet: A continuous-filter convolutional neural network for modeling quantum interactions.Advances in Neural Information Processing Systems, 30, 2017

Reference 72

Resolution: unresolved; no resolver link, observed 2026-08-07T14:36:15.171453Z

Source-reported events for the cited work

Unavailable: canonical work link unavailable.

Observation 5785a30b-cc5c-4076-b37f-f742ad2abe9c · outbound

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, 2016.

Self-Supervised Evolution Operator Learning for High-Dimensional Dynamical Systems Discovering governing equations from data by sparse identification of nonlinear dynamical systems.Proceedings of the national academy of sciences, 113(15):3932–3937, 2016

Reference 73

Resolution: unresolved; no resolver link, observed 2026-08-07T14:36:15.266916Z

Source-reported events for the cited work

Unavailable: canonical work link unavailable.

Observation 5e1fa7e3-603d-4c56-b29f-25b773ae93cd · outbound

This paper cites Descriptor-free collective variables from geometric graph neural networks.Journal of Chemical Theory and Computation, 20(24):10787–10797, 2024.

Self-Supervised Evolution Operator Learning for High-Dimensional Dynamical Systems Descriptor-free collective variables from geometric graph neural networks.Journal of Chemical Theory and Computation, 20(24):10787–10797, 2024

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Resolution: verified fuzzy; raw_fallback, observed 2026-08-07T14:36:24.240842Z

Source-reported events for the cited work

No event found in the named queried sources as of 2026-08-20T06:33:59.587034+00:00.

Observation 4adb6169-7266-4e57-85c7-1139bf1cb169 · outbound

This paper cites Characterizing metastable states with the help of machine learning.Journal of Chemical Theory and Compu- tation, 18(9):5195–5202, 2022.

Self-Supervised Evolution Operator Learning for High-Dimensional Dynamical Systems Characterizing metastable states with the help of machine learning.Journal of Chemical Theory and Compu- tation, 18(9):5195–5202, 2022

Reference 75

Resolution: verified fuzzy; raw_fallback, observed 2026-08-07T14:36:24.080502Z

Source-reported events for the cited work

No event found in the named queried sources as of 2026-08-20T06:33:59.587034+00:00.

Observation 6236443e-5111-4405-89e3-a5e33567e2b0 · outbound

This paper cites A variational approach to modeling slow processes in stochastic dynamical systems.Multiscale Modeling & Simulation, 11(2):635–655, 2013.

Self-Supervised Evolution Operator Learning for High-Dimensional Dynamical Systems A variational approach to modeling slow processes in stochastic dynamical systems.Multiscale Modeling & Simulation, 11(2):635–655, 2013

Reference 76

Resolution: unresolved; no resolver link, observed 2026-08-07T14:36:15.636746Z

Source-reported events for the cited work

Unavailable: canonical work link unavailable.

Observation f2826535-9ddc-4ec4-b959-90291c769517 · outbound

This paper cites Overview of the sampl5 host–guest challenge: Are we doing better?Journal of Computer-Aided Molecular Design, 31:1–19, 2017.

Self-Supervised Evolution Operator Learning for High-Dimensional Dynamical Systems Overview of the sampl5 host–guest challenge: Are we doing better?Journal of Computer-Aided Molecular Design, 31:1–19, 2017

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Resolution: verified fuzzy; raw_fallback, observed 2026-08-07T14:36:23.916595Z

Source-reported events for the cited work

No event found in the named queried sources as of 2026-08-20T06:33:59.587034+00:00.

Observation c4e320df-f9f7-4a72-8e6d-690ce32ca903 · outbound

This paper cites The role of water in host-guest interaction.Nature Communications, 12(1):93, 2021.

Self-Supervised Evolution Operator Learning for High-Dimensional Dynamical Systems The role of water in host-guest interaction.Nature Communications, 12(1):93, 2021

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Resolution: verified fuzzy; raw_fallback, observed 2026-08-07T14:36:23.788139Z

Source-reported events for the cited work

No event found in the named queried sources as of 2026-08-20T06:33:59.587034+00:00.

Observation d36bbb30-9bf3-4625-9f86-547e62012305 · outbound

This paper cites Cambridge University Press, 2000.

Self-Supervised Evolution Operator Learning for High-Dimensional Dynamical Systems Cambridge University Press, 2000

Reference 79

Resolution: verified fuzzy; raw_fallback, observed 2026-08-07T14:36:23.648689Z

Source-reported events for the cited work

No event found in the named queried sources as of 2026-08-20T06:33:59.587034+00:00.

Observation 86c6ef53-89bd-4773-8293-92804551099c · outbound

This paper cites Reviewing the oceanic niño index (oni) to enhance societal readiness for el niño’s impacts.International Journal of Disaster Risk Science, 11:394–403, 2020.

Self-Supervised Evolution Operator Learning for High-Dimensional Dynamical Systems Reviewing the oceanic niño index (oni) to enhance societal readiness for el niño’s impacts.International Journal of Disaster Risk Science, 11:394–403, 2020

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Resolution: verified fuzzy; raw_fallback, observed 2026-08-07T14:36:23.524327Z

Source-reported events for the cited work

No event found in the named queried sources as of 2026-08-20T06:33:59.587034+00:00.

Observation 22b51419-3b9c-403d-82c8-95f1b08a76fe · outbound

This paper cites Persistent effect of el niño on global economic growth.Science, 380(6649):1064–1069, 2023.

Self-Supervised Evolution Operator Learning for High-Dimensional Dynamical Systems Persistent effect of el niño on global economic growth.Science, 380(6649):1064–1069, 2023

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Resolution: verified fuzzy; raw_fallback, observed 2026-08-07T14:36:23.415758Z

Source-reported events for the cited work

No event found in the named queried sources as of 2026-08-20T06:33:59.587034+00:00.

Observation 50405fd4-6038-43d1-b748-d5e6d9352aaf · outbound

This paper cites Atmospheric teleconnections from the equatorial pacific.Monthly Weather Review, 97(3):163–172, 1969.

Self-Supervised Evolution Operator Learning for High-Dimensional Dynamical Systems Atmospheric teleconnections from the equatorial pacific.Monthly Weather Review, 97(3):163–172, 1969

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Resolution: verified fuzzy; raw_fallback, observed 2026-08-07T14:36:23.257528Z

Source-reported events for the cited work

No event found in the named queried sources as of 2026-08-20T06:33:59.587034+00:00.

Observation 41bb28ce-ead7-4a88-bcb5-a04891271dbf · outbound

This paper cites El nino southern oscillation phenomena.Nature, 302(5906):295–301, 1983.

Self-Supervised Evolution Operator Learning for High-Dimensional Dynamical Systems El nino southern oscillation phenomena.Nature, 302(5906):295–301, 1983

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Resolution: verified fuzzy; raw_fallback, observed 2026-08-07T14:36:23.107044Z

Source-reported events for the cited work

No event found in the named queried sources as of 2026-08-20T06:33:59.587034+00:00.

Observation 11be8b20-ec6a-4294-8ab5-18afef85703b · outbound

This paper cites Enso as an integrating concept in earth science.Science, 314(5806):1740–1745, 2006.

Self-Supervised Evolution Operator Learning for High-Dimensional Dynamical Systems Enso as an integrating concept in earth science.Science, 314(5806):1740–1745, 2006

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Resolution: verified fuzzy; raw_fallback, observed 2026-08-07T14:36:22.950781Z

Source-reported events for the cited work

No event found in the named queried sources as of 2026-08-20T06:33:59.587034+00:00.

Observation 98952e98-8e39-4360-bb43-5571e7ea638c · outbound

This paper cites Changing el niño–southern oscillation in a warming climate.Nature Reviews Earth & Environment, 2(9):628–644, 2021.

Self-Supervised Evolution Operator Learning for High-Dimensional Dynamical Systems Changing el niño–southern oscillation in a warming climate.Nature Reviews Earth & Environment, 2(9):628–644, 2021

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Resolution: verified fuzzy; raw_fallback, observed 2026-08-07T14:36:22.783608Z

Source-reported events for the cited work

No event found in the named queried sources as of 2026-08-20T06:33:59.587034+00:00.

Observation 4ed6e112-bcfa-49ea-92b0-633576c861c3 · outbound

This paper cites El niño and southern oscillation (enso): a review.

Self-Supervised Evolution Operator Learning for High-Dimensional Dynamical Systems El niño and southern oscillation (enso): a review

Reference 86

Resolution: verified fuzzy; raw_fallback, observed 2026-08-07T14:36:22.668102Z

Source-reported events for the cited work

No event found in the named queried sources as of 2026-08-20T06:33:59.587034+00:00.

Observation 9ec568a0-5015-451f-bd18-0cbc54cdf744 · outbound

This paper cites El niño–southern oscillation complexity.Nature, 559(7715):535–545, 2018.

Self-Supervised Evolution Operator Learning for High-Dimensional Dynamical Systems El niño–southern oscillation complexity.Nature, 559(7715):535–545, 2018

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Resolution: verified fuzzy; raw_fallback, observed 2026-08-07T14:36:22.511243Z

Source-reported events for the cited work

No event found in the named queried sources as of 2026-08-20T06:33:59.587034+00:00.

Observation 8fbf24d2-6dda-4b39-80ea-947f5ca24850 · outbound

This paper cites Spectral analysis of climate dynamics with operator-theoretic approaches.Nature Communications, 12(1):6570, 2021.

Self-Supervised Evolution Operator Learning for High-Dimensional Dynamical Systems Spectral analysis of climate dynamics with operator-theoretic approaches.Nature Communications, 12(1):6570, 2021

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Resolution: verified fuzzy; raw_fallback, observed 2026-08-07T14:36:22.375819Z

Source-reported events for the cited work

No event found in the named queried sources as of 2026-08-20T06:33:59.587034+00:00.

Observation 1f60b5d9-9b8d-4a97-af19-a2fafd1c21ed · outbound

This paper cites Deep learning for multi-year enso forecasts.Nature, 573(7775):568–572, 2019.

Self-Supervised Evolution Operator Learning for High-Dimensional Dynamical Systems Deep learning for multi-year enso forecasts.Nature, 573(7775):568–572, 2019

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Resolution: verified fuzzy; raw_fallback, observed 2026-08-07T14:36:22.215476Z

Source-reported events for the cited work

No event found in the named queried sources as of 2026-08-20T06:33:59.587034+00:00.

Observation 45e5534a-e5fb-4039-9c74-243eb6cba1e1 · outbound

This paper cites Climate Prediction Center - ONI — ori- gin.cpc.ncep.noaa.gov.

Self-Supervised Evolution Operator Learning for High-Dimensional Dynamical Systems Climate Prediction Center - ONI — ori- gin.cpc.ncep.noaa.gov

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Resolution: verified fuzzy; raw_fallback, observed 2026-08-07T14:36:22.057782Z

Source-reported events for the cited work

No event found in the named queried sources as of 2026-08-20T06:33:59.587034+00:00.

Observation f92b13bd-b0f9-4dd4-bba8-7f25def611d8 · outbound

This paper cites The ecmwf operational ensemble reanalysis–analysis system for ocean and sea ice: a description of the system and assessment.Ocean Science, 15(3):779–808, 2019.

Self-Supervised Evolution Operator Learning for High-Dimensional Dynamical Systems The ecmwf operational ensemble reanalysis–analysis system for ocean and sea ice: a description of the system and assessment.Ocean Science, 15(3):779–808, 2019

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Resolution: verified fuzzy; raw_fallback, observed 2026-08-07T14:36:21.891371Z

Source-reported events for the cited work

No event found in the named queried sources as of 2026-08-20T06:33:59.587034+00:00.

Observation fe3d92a0-247b-4a18-b995-9ec659484fbc · outbound

This paper cites ChaosBench: A Multi-Channel, Physics-Based Benchmark for Subseasonal-to-Seasonal Climate Prediction.

Self-Supervised Evolution Operator Learning for High-Dimensional Dynamical Systems ChaosBench: A Multi-Channel, Physics-Based Benchmark for Subseasonal-to-Seasonal Climate Prediction

Reference 92

Resolution: unresolved; no resolver link, observed 2026-08-07T14:36:17.455151Z

Source-reported events for the cited work

Unavailable: canonical work link unavailable.

Observation 0feb707e-5c67-4c6c-8acd-2f6c77d16b9b · outbound

This paper cites Deep residual learning for image recognition.

Self-Supervised Evolution Operator Learning for High-Dimensional Dynamical Systems Deep residual learning for image recognition

Reference 93

Resolution: unresolved; no resolver link, observed 2026-08-07T14:36:17.578291Z

Source-reported events for the cited work

Unavailable: canonical work link unavailable.

Observation af856916-1662-4c0b-b944-dc782cf8f479 · outbound

This paper cites A method for unsupervised learning of coherent spatiotemporal patterns in multiscale data.Proceedings of the National Academy of Sciences, 122(7):e2415786122, 2025.

Self-Supervised Evolution Operator Learning for High-Dimensional Dynamical Systems A method for unsupervised learning of coherent spatiotemporal patterns in multiscale data.Proceedings of the National Academy of Sciences, 122(7):e2415786122, 2025

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Resolution: verified fuzzy; raw_fallback, observed 2026-08-07T14:36:21.712158Z

Source-reported events for the cited work

No event found in the named queried sources as of 2026-08-20T06:33:59.587034+00:00.

Observation 0e9cd27f-be2d-4b4a-8229-c0a585ed2409 · outbound

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Self-Supervised Evolution Operator Learning for High-Dimensional Dynamical Systems Old and new matrix algebra useful for statistics.See www

Reference 95

Resolution: verified fuzzy; raw_fallback, observed 2026-08-07T14:36:21.562966Z

Source-reported events for the cited work

No event found in the named queried sources as of 2026-08-20T06:33:59.587034+00:00.

Observation b0c5452f-74e8-4087-a4ba-ac58d9a0e794 · outbound

This paper cites [Accessed 19-05-2025].

Self-Supervised Evolution Operator Learning for High-Dimensional Dynamical Systems [Accessed 19-05-2025]

Reference 96

Resolution: verified fuzzy; raw_fallback, observed 2026-08-07T14:36:21.441739Z

Source-reported events for the cited work

No event found in the named queried sources as of 2026-08-20T06:33:59.587034+00:00.

Observation 1a1a21bf-ad98-4c7d-a779-e42856dacf8e · outbound

This paper cites The lorenz attractor exists.Comptes Rendus de l’Académie des Sciences- Series I-Mathematics, 328(12):1197–1202, 1999.

Self-Supervised Evolution Operator Learning for High-Dimensional Dynamical Systems The lorenz attractor exists.Comptes Rendus de l’Académie des Sciences- Series I-Mathematics, 328(12):1197–1202, 1999

Reference 97

Resolution: verified fuzzy; raw_fallback, observed 2026-08-07T14:36:21.274586Z

Source-reported events for the cited work

No event found in the named queried sources as of 2026-08-20T06:33:59.587034+00:00.

Observation a297aba4-4e77-4af3-8d29-9056b400f615 · outbound

This paper cites Large sample analysis of the median heuristic.

Self-Supervised Evolution Operator Learning for High-Dimensional Dynamical Systems Large sample analysis of the median heuristic

Reference 98

Resolution: unresolved; no resolver link, observed 2026-08-07T14:36:18.066953Z

Source-reported events for the cited work

Unavailable: canonical work link unavailable.

Observation 27e61d6c-ef44-477b-8aa5-b7987e5372bc · outbound

This paper cites A unified framework for machine learning collective variables for enhanced sampling simulations: mlcolvar.The Journal of Chemical Physics, 159(1), 2023.

Self-Supervised Evolution Operator Learning for High-Dimensional Dynamical Systems A unified framework for machine learning collective variables for enhanced sampling simulations: mlcolvar.The Journal of Chemical Physics, 159(1), 2023

Reference 99

Resolution: verified fuzzy; raw_fallback, observed 2026-08-07T14:36:21.159664Z

Source-reported events for the cited work

No event found in the named queried sources as of 2026-08-20T06:33:59.587034+00:00.

Observation 8a9730c1-2da5-4388-8803-60e8c1111f7f · outbound

This paper cites Blind prediction of cyclohexane–water distribution coefficients from the sampl5 challenge.Journal of Computer-Aided Molecular Design, 30:927–944, 2016.

Self-Supervised Evolution Operator Learning for High-Dimensional Dynamical Systems Blind prediction of cyclohexane–water distribution coefficients from the sampl5 challenge.Journal of Computer-Aided Molecular Design, 30:927–944, 2016

Reference 100

Resolution: verified fuzzy; raw_fallback, observed 2026-08-07T14:36:21.003311Z

Source-reported events for the cited work

No event found in the named queried sources as of 2026-08-20T06:33:59.587034+00:00.

Coverage vector

Reference resolution

Outbound references

Pith citing papers