Modeling Partially Observed Nonlinear Dynamical Systems and Efficient Data Assimilation via Discrete-Time Conditional Gaussian Koopman Network | Paper Citation Record

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Modeling Partially Observed Nonlinear Dynamical Systems and Efficient Data Assimilation via Discrete-Time Conditional Gaussian Koopman Network Dynamical systems: examples of complex behaviour

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This paper cites Introduction to applied nonlinear dynamical systems and chaos , vol- ume 2.

Modeling Partially Observed Nonlinear Dynamical Systems and Efficient Data Assimilation via Discrete-Time Conditional Gaussian Koopman Network Introduction to applied nonlinear dynamical systems and chaos , vol- ume 2

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Modeling Partially Observed Nonlinear Dynamical Systems and Efficient Data Assimilation via Discrete-Time Conditional Gaussian Koopman Network Nonlinear dynamics and statistical theories for basic geophysical flows

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Modeling Partially Observed Nonlinear Dynamical Systems and Efficient Data Assimilation via Discrete-Time Conditional Gaussian Koopman Network Stochastic Methods for Modeling and Predicting Complex Dynamical Systems: Uncertainty Quantification, State Estimation, and Reduced-Order Models

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Modeling Partially Observed Nonlinear Dynamical Systems and Efficient Data Assimilation via Discrete-Time Conditional Gaussian Koopman Network Physics-informed neural networks for parameter learning of wildfire spread- ing

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Modeling Partially Observed Nonlinear Dynamical Systems and Efficient Data Assimilation via Discrete-Time Conditional Gaussian Koopman Network Development and application of a fluid mechanics analysis framework based on complex network theory

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Modeling Partially Observed Nonlinear Dynamical Systems and Efficient Data Assimilation via Discrete-Time Conditional Gaussian Koopman Network Nonlinear climate dynamics

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Modeling Partially Observed Nonlinear Dynamical Systems and Efficient Data Assimilation via Discrete-Time Conditional Gaussian Koopman Network Attribution of climate ex- treme events

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Modeling Partially Observed Nonlinear Dynamical Systems and Efficient Data Assimilation via Discrete-Time Conditional Gaussian Koopman Network Extreme events in turbulent flow

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Modeling Partially Observed Nonlinear Dynamical Systems and Efficient Data Assimilation via Discrete-Time Conditional Gaussian Koopman Network Introduction to PDEs and Waves for the Atmosphere and Ocean, volume 9

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Modeling Partially Observed Nonlinear Dynamical Systems and Efficient Data Assimilation via Discrete-Time Conditional Gaussian Koopman Network Model error, information barriers, state estimation and prediction in complex multiscale systems

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Modeling Partially Observed Nonlinear Dynamical Systems and Efficient Data Assimilation via Discrete-Time Conditional Gaussian Koopman Network Intermittency and the Lorenz model

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Modeling Partially Observed Nonlinear Dynamical Systems and Efficient Data Assimilation via Discrete-Time Conditional Gaussian Koopman Network Scientific machine learning for closure models in multiscale problems: a review

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Modeling Partially Observed Nonlinear Dynamical Systems and Efficient Data Assimilation via Discrete-Time Conditional Gaussian Koopman Network Learning about structural errors in models of complex dynamical systems

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Modeling Partially Observed Nonlinear Dynamical Systems and Efficient Data Assimilation via Discrete-Time Conditional Gaussian Koopman Network A physics-informed data-driven algorithm for ensemble forecast of complex turbulent systems

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Modeling Partially Observed Nonlinear Dynamical Systems and Efficient Data Assimilation via Discrete-Time Conditional Gaussian Koopman Network Data-driven discovery of partial differential equations

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Modeling Partially Observed Nonlinear Dynamical Systems and Efficient Data Assimilation via Discrete-Time Conditional Gaussian Koopman Network Sparse dynam- ics for partial differential equations

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This paper cites Strategies for reduced-order models for predicting the statistical responses and uncertainty quantification in complex turbulent dynamical systems.

Modeling Partially Observed Nonlinear Dynamical Systems and Efficient Data Assimilation via Discrete-Time Conditional Gaussian Koopman Network Strategies for reduced-order models for predicting the statistical responses and uncertainty quantification in complex turbulent dynamical systems

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Modeling Partially Observed Nonlinear Dynamical Systems and Efficient Data Assimilation via Discrete-Time Conditional Gaussian Koopman Network The gnat method for nonlinear model reduction: effective implementation and application to computational fluid dynamics and turbulent flows

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Modeling Partially Observed Nonlinear Dynamical Systems and Efficient Data Assimilation via Discrete-Time Conditional Gaussian Koopman Network Reduced-order modelling for flow control, volume 528

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This paper cites A causality-based learning approach for discovering the underlying dynamics of complex systems from partial observations with stochastic parame- terization.

Modeling Partially Observed Nonlinear Dynamical Systems and Efficient Data Assimilation via Discrete-Time Conditional Gaussian Koopman Network A causality-based learning approach for discovering the underlying dynamics of complex systems from partial observations with stochastic parame- terization

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Modeling Partially Observed Nonlinear Dynamical Systems and Efficient Data Assimilation via Discrete-Time Conditional Gaussian Koopman Network CEBoosting: Online sparse identification of dynamical systems with regime switching by causation entropy boosting

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Modeling Partially Observed Nonlinear Dynamical Systems and Efficient Data Assimilation via Discrete-Time Conditional Gaussian Koopman Network Minimum reduced-order models via causal inference

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Modeling Partially Observed Nonlinear Dynamical Systems and Efficient Data Assimilation via Discrete-Time Conditional Gaussian Koopman Network Solving and learning nonlinear pdes with Gaussian processes

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Modeling Partially Observed Nonlinear Dynamical Systems and Efficient Data Assimilation via Discrete-Time Conditional Gaussian Koopman Network Deep learning

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Modeling Partially Observed Nonlinear Dynamical Systems and Efficient Data Assimilation via Discrete-Time Conditional Gaussian Koopman Network Imagenet classification with deep convolutional neural networks

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Modeling Partially Observed Nonlinear Dynamical Systems and Efficient Data Assimilation via Discrete-Time Conditional Gaussian Koopman Network Learning representations by back-propagating errors

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Modeling Partially Observed Nonlinear Dynamical Systems and Efficient Data Assimilation via Discrete-Time Conditional Gaussian Koopman Network Convolutional networks for images, speech, and time series

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Modeling Partially Observed Nonlinear Dynamical Systems and Efficient Data Assimilation via Discrete-Time Conditional Gaussian Koopman Network Handwritten digit recognition with a back-propagation network

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Modeling Partially Observed Nonlinear Dynamical Systems and Efficient Data Assimilation via Discrete-Time Conditional Gaussian Koopman Network Physics-informed machine learning

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Modeling Partially Observed Nonlinear Dynamical Systems and Efficient Data Assimilation via Discrete-Time Conditional Gaussian Koopman Network Turbulence modeling in the age of data

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Modeling Partially Observed Nonlinear Dynamical Systems and Efficient Data Assimilation via Discrete-Time Conditional Gaussian Koopman Network Learning dynamical systems from data: An introduction to physics-guided deep learning

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Modeling Partially Observed Nonlinear Dynamical Systems and Efficient Data Assimilation via Discrete-Time Conditional Gaussian Koopman Network Physics-informed machine learning ap- proach for reconstructing reynolds stress modeling discrepancies based on dns data

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Modeling Partially Observed Nonlinear Dynamical Systems and Efficient Data Assimilation via Discrete-Time Conditional Gaussian Koopman Network Physics-informed machine learning approach for augmenting turbulence models: A comprehensive framework

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Modeling Partially Observed Nonlinear Dynamical Systems and Efficient Data Assimilation via Discrete-Time Conditional Gaussian Koopman Network Approximation by superpositions of a sigmoidal function

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Modeling Partially Observed Nonlinear Dynamical Systems and Efficient Data Assimilation via Discrete-Time Conditional Gaussian Koopman Network Multilayer feedforward networks are universal approximators

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Modeling Partially Observed Nonlinear Dynamical Systems and Efficient Data Assimilation via Discrete-Time Conditional Gaussian Koopman Network Deep residual learning for image recognition

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Modeling Partially Observed Nonlinear Dynamical Systems and Efficient Data Assimilation via Discrete-Time Conditional Gaussian Koopman Network Neural ordi- nary differential equations

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Modeling Partially Observed Nonlinear Dynamical Systems and Efficient Data Assimilation via Discrete-Time Conditional Gaussian Koopman Network Physics-informed neural net- works: A deep learning framework for solving forward and inverse problems involving nonlinear partial differential equations

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Modeling Partially Observed Nonlinear Dynamical Systems and Efficient Data Assimilation via Discrete-Time Conditional Gaussian Koopman Network Learn- ing nonlinear operators via deeponet based on the universal approximation theorem of op- erators

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Modeling Partially Observed Nonlinear Dynamical Systems and Efficient Data Assimilation via Discrete-Time Conditional Gaussian Koopman Network Fourier neural operator for paramet- ric partial differential equations

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Modeling Partially Observed Nonlinear Dynamical Systems and Efficient Data Assimilation via Discrete-Time Conditional Gaussian Koopman Network Neural dynamical operator: Continuous spatial-temporal model with gradient-based and derivative-free optimization methods

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Modeling Partially Observed Nonlinear Dynamical Systems and Efficient Data Assimilation via Discrete-Time Conditional Gaussian Koopman Network MODNO: Multi-operator learning with distributed neural operators

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Modeling Partially Observed Nonlinear Dynamical Systems and Efficient Data Assimilation via Discrete-Time Conditional Gaussian Koopman Network B-deeponet: An enhanced Bayesian deeponet for solving noisy parametric pdes using accelerated replica exchange sgld.Journal of Computational Physics, 473:111713, 2023

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Modeling Partially Observed Nonlinear Dynamical Systems and Efficient Data Assimilation via Discrete-Time Conditional Gaussian Koopman Network A comprehensive study of non-adaptive and residual-based adaptive sampling for physics-informed neural networks

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Modeling Partially Observed Nonlinear Dynamical Systems and Efficient Data Assimilation via Discrete-Time Conditional Gaussian Koopman Network Auto-encoding variational bayes, 2013

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Modeling Partially Observed Nonlinear Dynamical Systems and Efficient Data Assimilation via Discrete-Time Conditional Gaussian Koopman Network Generative adversarial networks

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Modeling Partially Observed Nonlinear Dynamical Systems and Efficient Data Assimilation via Discrete-Time Conditional Gaussian Koopman Network Enforc- ing statistical constraints in generative adversarial networks for modeling chaotic dynamical systems

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Modeling Partially Observed Nonlinear Dynamical Systems and Efficient Data Assimilation via Discrete-Time Conditional Gaussian Koopman Network Denoising diffusion probabilistic models

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Modeling Partially Observed Nonlinear Dynamical Systems and Efficient Data Assimilation via Discrete-Time Conditional Gaussian Koopman Network Score-Based Generative Modeling through Stochastic Differential Equations

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Modeling Partially Observed Nonlinear Dynamical Systems and Efficient Data Assimilation via Discrete-Time Conditional Gaussian Koopman Network Data-driven stochastic closure modeling via conditional diffusion model and neural operator

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This paper cites Condi- tional neural field latent diffusion model for generating spatiotemporal turbulence.

Modeling Partially Observed Nonlinear Dynamical Systems and Efficient Data Assimilation via Discrete-Time Conditional Gaussian Koopman Network Condi- tional neural field latent diffusion model for generating spatiotemporal turbulence

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This paper cites Can diffusion models capture extreme event statistics? Computer Methods in Applied Mechanics and Engineering , 435:117589, 2025.

Modeling Partially Observed Nonlinear Dynamical Systems and Efficient Data Assimilation via Discrete-Time Conditional Gaussian Koopman Network Can diffusion models capture extreme event statistics? Computer Methods in Applied Mechanics and Engineering , 435:117589, 2025

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Observation bee97bf5-c151-4f36-9caa-cce714cb63f6 · outbound

This paper cites Generative learning of the solution of parametric partial differential equations using guided diffusion models and vir- tual observations.

Modeling Partially Observed Nonlinear Dynamical Systems and Efficient Data Assimilation via Discrete-Time Conditional Gaussian Koopman Network Generative learning of the solution of parametric partial differential equations using guided diffusion models and vir- tual observations

Reference 55

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Observation 77b36e17-8cfb-4d0b-863f-22339ba847e5 · outbound

This paper cites Active learning literature survey.

Modeling Partially Observed Nonlinear Dynamical Systems and Efficient Data Assimilation via Discrete-Time Conditional Gaussian Koopman Network Active learning literature survey

Reference 56

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This paper cites Learning active learning from data.

Modeling Partially Observed Nonlinear Dynamical Systems and Efficient Data Assimilation via Discrete-Time Conditional Gaussian Koopman Network Learning active learning from data

Reference 57

Resolution: verified fuzzy; raw_fallback, observed 2026-08-06T18:18:20.008973Z

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Observation 3b8130e2-f8cf-4c60-8fc1-31aebd1bebcc · outbound

This paper cites Simulation-based optimal Bayesian experimental de- sign for nonlinear systems.

Modeling Partially Observed Nonlinear Dynamical Systems and Efficient Data Assimilation via Discrete-Time Conditional Gaussian Koopman Network Simulation-based optimal Bayesian experimental de- sign for nonlinear systems

Reference 58

Resolution: verified fuzzy; raw_fallback, observed 2026-08-06T18:18:19.734747Z

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Observation f8c73669-d3b3-4c98-9024-daf823120949 · outbound

This paper cites Bayesian inference in physics.Reviews of Modern Physics, 83(3):943– 999, 2011.

Modeling Partially Observed Nonlinear Dynamical Systems and Efficient Data Assimilation via Discrete-Time Conditional Gaussian Koopman Network Bayesian inference in physics.Reviews of Modern Physics, 83(3):943– 999, 2011

Reference 59

Resolution: verified fuzzy; raw_fallback, observed 2026-08-06T18:18:19.473976Z

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Observation 4e457796-f40a-4082-9b3a-b5622e28a19d · outbound

This paper cites Active-learning-driven surrogate model- ing for efficient simulation of parametric nonlinear systems.

Modeling Partially Observed Nonlinear Dynamical Systems and Efficient Data Assimilation via Discrete-Time Conditional Gaussian Koopman Network Active-learning-driven surrogate model- ing for efficient simulation of parametric nonlinear systems

Reference 60

Resolution: verified fuzzy; raw_fallback, observed 2026-08-06T18:18:19.242430Z

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Observation 310cb94f-aa3b-4f47-b17d-5abc12ea8cf4 · outbound

This paper cites Optimum experimental designs, with SAS, volume 34.

Modeling Partially Observed Nonlinear Dynamical Systems and Efficient Data Assimilation via Discrete-Time Conditional Gaussian Koopman Network Optimum experimental designs, with SAS, volume 34

Reference 61

Resolution: verified fuzzy; raw_fallback, observed 2026-08-06T18:18:19.199395Z

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Observation d552e5ae-b5d6-40ec-8b6b-44158288af8b · outbound

This paper cites Active Learning of Model Discrepancy with Bayesian Experimental Design.

Modeling Partially Observed Nonlinear Dynamical Systems and Efficient Data Assimilation via Discrete-Time Conditional Gaussian Koopman Network Active Learning of Model Discrepancy with Bayesian Experimental Design

Reference 62

Resolution: verified exact; local_arxiv, observed 2026-08-06T18:18:09.251956Z

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Observation 63f9e1a8-daa5-4ae0-ae04-9c06bc985fc3 · outbound

This paper cites A new approach to linear filtering and prediction problems.

Modeling Partially Observed Nonlinear Dynamical Systems and Efficient Data Assimilation via Discrete-Time Conditional Gaussian Koopman Network A new approach to linear filtering and prediction problems

Reference 63

Resolution: verified fuzzy; raw_fallback, observed 2026-08-06T18:18:19.155926Z

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Observation 7ae86006-bfdf-4cf3-b25f-b97953472f95 · outbound

This paper cites New results in linear filtering and prediction theory.

Modeling Partially Observed Nonlinear Dynamical Systems and Efficient Data Assimilation via Discrete-Time Conditional Gaussian Koopman Network New results in linear filtering and prediction theory

Reference 64

Resolution: verified fuzzy; raw_fallback, observed 2026-08-06T18:18:19.117654Z

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This paper cites Filtering complex turbulent systems.

Modeling Partially Observed Nonlinear Dynamical Systems and Efficient Data Assimilation via Discrete-Time Conditional Gaussian Koopman Network Filtering complex turbulent systems

Reference 65

Resolution: verified fuzzy; raw_fallback, observed 2026-08-06T18:18:19.055642Z

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Observation c463fe46-92b4-4384-a106-733410048ef7 · outbound

This paper cites Data assimilation.

Modeling Partially Observed Nonlinear Dynamical Systems and Efficient Data Assimilation via Discrete-Time Conditional Gaussian Koopman Network Data assimilation

Reference 66

Resolution: verified fuzzy; raw_fallback, observed 2026-08-06T18:18:19.040899Z

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Observation 26cd8c3a-50e5-4ae6-b5ee-3f85bc032f5a · outbound

This paper cites Learning stochastic closures using ensemble Kalman inversion.

Modeling Partially Observed Nonlinear Dynamical Systems and Efficient Data Assimilation via Discrete-Time Conditional Gaussian Koopman Network Learning stochastic closures using ensemble Kalman inversion

Reference 67

Resolution: verified fuzzy; raw_fallback, observed 2026-08-06T18:18:18.915863Z

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Observation 8e625b9e-2b1c-4e52-87a8-a9650e09e964 · outbound

This paper cites Ensemble Kalman inversion for sparse learning of dynamical systems from time-averaged data.

Modeling Partially Observed Nonlinear Dynamical Systems and Efficient Data Assimilation via Discrete-Time Conditional Gaussian Koopman Network Ensemble Kalman inversion for sparse learning of dynamical systems from time-averaged data

Reference 68

Resolution: verified fuzzy; raw_fallback, observed 2026-08-06T18:18:18.777531Z

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Observation 8bf76db9-a27a-4171-b37a-33f364dca0f3 · outbound

This paper cites Ensemble Kalman methods for inverse problems.

Modeling Partially Observed Nonlinear Dynamical Systems and Efficient Data Assimilation via Discrete-Time Conditional Gaussian Koopman Network Ensemble Kalman methods for inverse problems

Reference 69

Resolution: verified fuzzy; raw_fallback, observed 2026-08-06T18:18:18.650952Z

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Observation 15140f42-66bb-419b-b9fc-a0a5e02cd266 · outbound

This paper cites An efficient continuous data assimilation al- gorithm for the sabra shell model of turbulence.

Modeling Partially Observed Nonlinear Dynamical Systems and Efficient Data Assimilation via Discrete-Time Conditional Gaussian Koopman Network An efficient continuous data assimilation al- gorithm for the sabra shell model of turbulence

Reference 70

Resolution: verified fuzzy; raw_fallback, observed 2026-08-06T18:18:18.540810Z

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Observation da7a79bf-79fd-4514-a3ea-611eb1821428 · outbound

This paper cites Efficient statistically accurate algorithms for the fokker– planck equation in large dimensions.

Modeling Partially Observed Nonlinear Dynamical Systems and Efficient Data Assimilation via Discrete-Time Conditional Gaussian Koopman Network Efficient statistically accurate algorithms for the fokker– planck equation in large dimensions

Reference 71

Resolution: verified fuzzy; raw_fallback, observed 2026-08-06T18:18:18.437156Z

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Observation 32fe0189-3824-4182-864b-2ee3389c38c9 · outbound

This paper cites an unresolved cited work.

Modeling Partially Observed Nonlinear Dynamical Systems and Efficient Data Assimilation via Discrete-Time Conditional Gaussian Koopman Network Unresolved cited work

Reference 72

Resolution: unresolved; raw_fallback, observed 2026-08-06T18:18:18.323906Z

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Observation 9467820f-1672-4399-b079-cdb0cb872015 · outbound

This paper cites An ensemble Kalman-Bucy filter for continuous data assimilation.

Modeling Partially Observed Nonlinear Dynamical Systems and Efficient Data Assimilation via Discrete-Time Conditional Gaussian Koopman Network An ensemble Kalman-Bucy filter for continuous data assimilation

Reference 73

Resolution: verified fuzzy; raw_fallback, observed 2026-08-06T18:18:18.214754Z

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Observation 57695817-3756-4fb3-8e57-220b54edb71f · outbound

This paper cites The ensemble Kalman filter: Theoretical formulation and practical imple- mentation.

Modeling Partially Observed Nonlinear Dynamical Systems and Efficient Data Assimilation via Discrete-Time Conditional Gaussian Koopman Network The ensemble Kalman filter: Theoretical formulation and practical imple- mentation

Reference 74

Resolution: verified fuzzy; raw_fallback, observed 2026-08-06T18:18:18.067135Z

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Observation 7d4d61e9-339b-413d-9a42-c3dc5196ea18 · outbound

This paper cites Ensemble data assimilation without perturbed observations.

Modeling Partially Observed Nonlinear Dynamical Systems and Efficient Data Assimilation via Discrete-Time Conditional Gaussian Koopman Network Ensemble data assimilation without perturbed observations

Reference 75

Resolution: verified fuzzy; raw_fallback, observed 2026-08-06T18:18:17.958554Z

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Observation cd9776a2-ee5e-407e-8910-39f953485a38 · outbound

This paper cites Analysis scheme in the ensemble Kalman filter.

Modeling Partially Observed Nonlinear Dynamical Systems and Efficient Data Assimilation via Discrete-Time Conditional Gaussian Koopman Network Analysis scheme in the ensemble Kalman filter

Reference 76

Resolution: verified fuzzy; raw_fallback, observed 2026-08-06T18:18:17.825636Z

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Observation 6bf360be-a39c-4e3e-978a-5db6598738a7 · outbound

This paper cites Data-driven variational multiscale reduced order models.

Modeling Partially Observed Nonlinear Dynamical Systems and Efficient Data Assimilation via Discrete-Time Conditional Gaussian Koopman Network Data-driven variational multiscale reduced order models

Reference 77

Resolution: verified fuzzy; raw_fallback, observed 2026-08-06T18:18:17.657547Z

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Observation f6dc5794-e138-43a4-86ef-99ff87814f13 · outbound

This paper cites Dynamic data-driven reduced-order models.

Modeling Partially Observed Nonlinear Dynamical Systems and Efficient Data Assimilation via Discrete-Time Conditional Gaussian Koopman Network Dynamic data-driven reduced-order models

Reference 78

Resolution: unresolved; no resolver link, observed 2026-08-06T18:18:03.650422Z

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Observation 0cbeab2f-c431-44d6-b20c-432c2de51d92 · outbound

This paper cites Data-driven pod- galerkin reduced order model for turbulent flows.

Modeling Partially Observed Nonlinear Dynamical Systems and Efficient Data Assimilation via Discrete-Time Conditional Gaussian Koopman Network Data-driven pod- galerkin reduced order model for turbulent flows

Reference 79

Resolution: verified fuzzy; raw_fallback, observed 2026-08-06T18:18:17.468059Z

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Observation 4396acc0-9d25-4022-9cf6-38ef0562bde6 · outbound

This paper cites Data-driven model reduction, Wiener projections, and the Koopman-Mori-Zwanzig formalism.

Modeling Partially Observed Nonlinear Dynamical Systems and Efficient Data Assimilation via Discrete-Time Conditional Gaussian Koopman Network Data-driven model reduction, Wiener projections, and the Koopman-Mori-Zwanzig formalism

Reference 80

Resolution: verified fuzzy; raw_fallback, observed 2026-08-06T18:18:17.256891Z

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Observation 74ffe837-189f-4b26-8fa2-f80b5d123e85 · outbound

This paper cites Operator inference driven data assimilation for high fidelity neutron transport.

Modeling Partially Observed Nonlinear Dynamical Systems and Efficient Data Assimilation via Discrete-Time Conditional Gaussian Koopman Network Operator inference driven data assimilation for high fidelity neutron transport

Reference 81

Resolution: verified fuzzy; raw_fallback, observed 2026-08-06T18:18:17.033438Z

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Observation fe3c7acc-d678-4347-b92a-c996fd1d2f67 · outbound

This paper cites Combining stochastic parameterized reduced-order models with machine learning for data assimilation and uncertainty quan- tification with partial observations.

Modeling Partially Observed Nonlinear Dynamical Systems and Efficient Data Assimilation via Discrete-Time Conditional Gaussian Koopman Network Combining stochastic parameterized reduced-order models with machine learning for data assimilation and uncertainty quan- tification with partial observations

Reference 82

Resolution: verified fuzzy; raw_fallback, observed 2026-08-06T18:18:16.870991Z

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Observation 56451771-9170-46b2-b37c-da8c13b01117 · outbound

This paper cites Stochastic parameterization: Toward a new view of weather and climate models.

Modeling Partially Observed Nonlinear Dynamical Systems and Efficient Data Assimilation via Discrete-Time Conditional Gaussian Koopman Network Stochastic parameterization: Toward a new view of weather and climate models

Reference 83

Resolution: verified fuzzy; raw_fallback, observed 2026-08-06T18:18:16.714348Z

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Observation e551b370-da73-49e1-b022-6c73893474fd · outbound

This paper cites Toward a stochastic parameterization of ocean mesoscale eddies.

Modeling Partially Observed Nonlinear Dynamical Systems and Efficient Data Assimilation via Discrete-Time Conditional Gaussian Koopman Network Toward a stochastic parameterization of ocean mesoscale eddies

Reference 84

Resolution: verified fuzzy; raw_fallback, observed 2026-08-06T18:18:16.537076Z

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Observation 6f6f1fa7-9f98-4e45-a9e6-5e21d53a4214 · outbound

This paper cites Simulating weather regimes: Impact of model resolution and stochastic parameterization.

Modeling Partially Observed Nonlinear Dynamical Systems and Efficient Data Assimilation via Discrete-Time Conditional Gaussian Koopman Network Simulating weather regimes: Impact of model resolution and stochastic parameterization

Reference 85

Resolution: verified fuzzy; raw_fallback, observed 2026-08-06T18:18:16.298367Z

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Observation 353ad828-f6c1-444f-a187-b6b233665796 · outbound

This paper cites A stochastic precipitating quasi-geostrophic model.

Modeling Partially Observed Nonlinear Dynamical Systems and Efficient Data Assimilation via Discrete-Time Conditional Gaussian Koopman Network A stochastic precipitating quasi-geostrophic model

Reference 86

Resolution: verified fuzzy; raw_fallback, observed 2026-08-06T18:18:16.111055Z

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Observation 0c92cd25-a91f-4bcb-8ce6-b8a46290ba92 · outbound

This paper cites Anderson.

Modeling Partially Observed Nonlinear Dynamical Systems and Efficient Data Assimilation via Discrete-Time Conditional Gaussian Koopman Network Anderson

Reference 87

Resolution: verified fuzzy; raw_fallback, observed 2026-08-06T18:18:15.952343Z

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Observation d1b1f6df-eefc-4d79-8f7e-99efe6c4d694 · outbound

This paper cites State, global, and local parameter estimation using local ensemble Kalman filters: Applications to online machine learning of chaotic dynamics.

Modeling Partially Observed Nonlinear Dynamical Systems and Efficient Data Assimilation via Discrete-Time Conditional Gaussian Koopman Network State, global, and local parameter estimation using local ensemble Kalman filters: Applications to online machine learning of chaotic dynamics

Reference 88

Resolution: verified fuzzy; raw_fallback, observed 2026-08-06T18:18:15.820955Z

Source-reported events for the cited work

No event found in the named queried sources as of 2026-08-08T06:32:00.761636+00:00.

Observation e278448f-1926-445a-bf1a-bd24711c4afd · 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.

Modeling Partially Observed Nonlinear Dynamical Systems and Efficient Data Assimilation via Discrete-Time Conditional Gaussian Koopman Network 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 89

Resolution: verified fuzzy; raw_fallback, observed 2026-08-06T18:18:15.639190Z

Source-reported events for the cited work

No event found in the named queried sources as of 2026-08-08T06:32:00.761636+00:00.

Observation f9397b0d-050d-4dcc-bed9-d62ef64def46 · outbound

This paper cites Deep learning to represent sub- grid processes in climate models.

Modeling Partially Observed Nonlinear Dynamical Systems and Efficient Data Assimilation via Discrete-Time Conditional Gaussian Koopman Network Deep learning to represent sub- grid processes in climate models

Reference 90

Resolution: verified fuzzy; raw_fallback, observed 2026-08-06T18:18:15.509013Z

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No event found in the named queried sources as of 2026-08-08T06:32:00.761636+00:00.

Observation e4fa1b7e-8d8c-4448-9374-2d6e10cbc78e · outbound

This paper cites Machine learning for model error inference and correction.

Modeling Partially Observed Nonlinear Dynamical Systems and Efficient Data Assimilation via Discrete-Time Conditional Gaussian Koopman Network Machine learning for model error inference and correction

Reference 91

Resolution: verified fuzzy; raw_fallback, observed 2026-08-06T18:18:15.356372Z

Source-reported events for the cited work

No event found in the named queried sources as of 2026-08-08T06:32:00.761636+00:00.

Observation bbe32bdd-e13d-43b3-9622-110c17fe34d1 · outbound

This paper cites Combining data as- similation and machine learning to infer unresolved scale parametrization.

Modeling Partially Observed Nonlinear Dynamical Systems and Efficient Data Assimilation via Discrete-Time Conditional Gaussian Koopman Network Combining data as- similation and machine learning to infer unresolved scale parametrization

Reference 92

Resolution: verified fuzzy; raw_fallback, observed 2026-08-06T18:18:15.232138Z

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Observation 026062a4-5aee-46f7-b3ae-f10c9ddf405c · outbound

This paper cites Statistical variational data assimilation.

Modeling Partially Observed Nonlinear Dynamical Systems and Efficient Data Assimilation via Discrete-Time Conditional Gaussian Koopman Network Statistical variational data assimilation

Reference 93

Resolution: verified fuzzy; raw_fallback, observed 2026-08-06T18:18:15.068309Z

Source-reported events for the cited work

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Observation d0be550c-e129-4d51-ac59-ec5b6ddbc5cf · outbound

This paper cites Multi-domain encoder–decoder neural networks for latent data assimi- lation in dynamical systems.

Modeling Partially Observed Nonlinear Dynamical Systems and Efficient Data Assimilation via Discrete-Time Conditional Gaussian Koopman Network Multi-domain encoder–decoder neural networks for latent data assimi- lation in dynamical systems

Reference 94

Resolution: verified fuzzy; raw_fallback, observed 2026-08-06T18:18:14.972198Z

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No event found in the named queried sources as of 2026-08-08T06:32:00.761636+00:00.

Observation 395b5937-441d-4610-8fbc-6943fe9ac2a2 · outbound

This paper cites Deep learning-enhanced ensemble-based data assimilation for high-dimensional nonlinear dy- namical systems.

Modeling Partially Observed Nonlinear Dynamical Systems and Efficient Data Assimilation via Discrete-Time Conditional Gaussian Koopman Network Deep learning-enhanced ensemble-based data assimilation for high-dimensional nonlinear dy- namical systems

Reference 95

Resolution: verified fuzzy; raw_fallback, observed 2026-08-06T18:18:14.929375Z

Source-reported events for the cited work

No event found in the named queried sources as of 2026-08-08T06:32:00.761636+00:00.

Observation 4d9444a6-b7f4-40e3-b7f3-778a76b758dc · outbound

This paper cites Kalmannet: Neural network aided Kalman filtering for partially known dynamics.

Modeling Partially Observed Nonlinear Dynamical Systems and Efficient Data Assimilation via Discrete-Time Conditional Gaussian Koopman Network Kalmannet: Neural network aided Kalman filtering for partially known dynamics

Reference 96

Resolution: verified fuzzy; raw_fallback, observed 2026-08-06T18:18:14.757283Z

Source-reported events for the cited work

No event found in the named queried sources as of 2026-08-08T06:32:00.761636+00:00.

Observation 1effe8b4-8ce7-4065-a582-42a5c28dd26c · outbound

This paper cites Data assim- ilation networks.

Modeling Partially Observed Nonlinear Dynamical Systems and Efficient Data Assimilation via Discrete-Time Conditional Gaussian Koopman Network Data assim- ilation networks

Reference 97

Resolution: verified fuzzy; raw_fallback, observed 2026-08-06T18:18:14.546889Z

Source-reported events for the cited work

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Observation 1cc17cc3-cf05-437a-8db9-ca470abd7202 · outbound

This paper cites Autodifferentiable ensemble Kalman filters.

Modeling Partially Observed Nonlinear Dynamical Systems and Efficient Data Assimilation via Discrete-Time Conditional Gaussian Koopman Network Autodifferentiable ensemble Kalman filters

Reference 98

Resolution: verified fuzzy; raw_fallback, observed 2026-08-06T18:18:14.326700Z

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Observation b32ada88-b713-4bcb-b449-a8750ceacbb5 · outbound

This paper cites Reduced-order autodifferentiable ensemble Kalman filters.

Modeling Partially Observed Nonlinear Dynamical Systems and Efficient Data Assimilation via Discrete-Time Conditional Gaussian Koopman Network Reduced-order autodifferentiable ensemble Kalman filters

Reference 99

Resolution: verified fuzzy; raw_fallback, observed 2026-08-06T18:18:14.207083Z

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Observation 2af8f208-8f3f-47ff-bee0-dc60abeb7290 · outbound

This paper cites CGNSDE: Conditional Gaussian neural stochastic differential equation for modeling complex systems and data assimilation.

Modeling Partially Observed Nonlinear Dynamical Systems and Efficient Data Assimilation via Discrete-Time Conditional Gaussian Koopman Network CGNSDE: Conditional Gaussian neural stochastic differential equation for modeling complex systems and data assimilation

Reference 100

Resolution: verified fuzzy; raw_fallback, observed 2026-08-06T18:18:13.882409Z

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