Cover Image

Deep Learning of the Evolution Operator Enables Forecasting of Out-of-Training Dynamics in Chaotic Systems

Fuente: arXiv

Saved in:

Bibliographic Details
Main Authors:	Shokar, Ira J. S., Haynes, Peter H., Kerswell, Rich R.
Format:	Preprint
Published:	2025
Subjects:	Machine Learning Dynamical Systems Chaotic Dynamics
Online Access:	Acceder al recurso
Tags:	Add Tag No Tags, Be the first to tag this record!

Similar Items

Conditioning on PDE Parameters to Generalise Deep Learning Emulation of Stochastic and Chaotic Dynamics
by: Shokar, Ira J. S., et al.
Published: (2025)

Machine-Precision Prediction of Low-Dimensional Chaotic Systems
by: Schötz, Christof, et al.
Published: (2025)

Horizon-Constrained Rashomon Sets for Chaotic Forecasting
by: Kale, Gauri, et al.
Published: (2026)

Dynamics-Informed Deep Learning for Predicting Extreme Events
by: Katsidoniotaki, Eirini, et al.
Published: (2026)

Out-of-Domain Generalization in Dynamical Systems Reconstruction
by: Göring, Niclas, et al.
Published: (2024)

Predicting Chaotic System Behavior using Machine Learning Techniques
by: Rao, Huaiyuan, et al.
Published: (2024)

Data-Driven Modeling and Forecasting of Chaotic Dynamics on Inertial Manifolds Constructed as Spectral Submanifolds
by: Liu, Aihui, et al.
Published: (2023)

PINN-Obs: Physics-Informed Neural Network-Based Observer for Nonlinear Dynamical Systems
by: Farkane, Ayoub, et al.
Published: (2025)

CEBoosting: Online Sparse Identification of Dynamical Systems with Regime Switching by Causation Entropy Boosting
by: Chen, Chuanqi, et al.
Published: (2023)

A Deep Learning Framework for Identifying Weakly Chaotic, Strongly Chaotic, Resonant and Non-resonant Orbits in the Generalized Kicked Rotator
by: Zu, Jian, et al.
Published: (2025)

Machine Learning for Predicting Chaotic Systems
by: Schötz, Christof, et al.
Published: (2024)

FEG-Pro: Forecast-Error Growth Profiling for Finite-Horizon Instability Analysis of Nonlinear Time Series
by: Velichko, Andrei, et al.
Published: (2026)

Backpropagation on Dynamical Networks
by: Tan, Eugene, et al.
Published: (2022)

Learning the Chaotic and Regular Nature of Trajectories in Hamiltonian Systems with Lagrangian descriptors
by: López, Javier Jiménez, et al.
Published: (2024)

Integrating Multimodal Data for Joint Generative Modeling of Complex Dynamics
by: Brenner, Manuel, et al.
Published: (2022)

Retrodicting Chaotic Systems: An Algorithmic Information Theory Approach
by: Dingle, Kamal, et al.
Published: (2025)

Stochastic Latent Transformer: Efficient Modelling of Stochastically Forced Zonal Jets
by: Shokar, Ira J. S., et al.
Published: (2023)

Designing Chaotic Attractors: A Semi-supervised Approach
by: Kabayama, Tempei, et al.
Published: (2024)

Incorporating Coupling Knowledge into Echo State Networks for Learning Spatiotemporally Chaotic Dynamics
by: Chu, Kuei-Jan, et al.
Published: (2025)

Weighted Birkhoff Averages Accelerate Data-Driven Methods
by: Bou-Sakr-El-Tayar, Maria, et al.
Published: (2025)

Minimal Deterministic Echo State Networks Outperform Random Reservoirs in Learning Chaotic Dynamics
by: Martinuzzi, Francesco
Published: (2025)

Optimal Recurrent Network Topologies for Dynamical Systems Reconstruction
by: Hemmer, Christoph Jürgen, et al.
Published: (2024)

A Numerical Study of Chaotic Dynamics of K-S Equation with FNOs
by: Khetrapal, Surbhi, et al.
Published: (2024)

Invariant Measures in Time-Delay Coordinates for Unique Dynamical System Identification
by: Botvinick-Greenhouse, Jonah, et al.
Published: (2024)

Controlling Dynamical Systems into Unseen Target States Using Machine Learning
by: Köglmayr, Daniel, et al.
Published: (2024)

True Zero-Shot Inference of Dynamical Systems Preserving Long-Term Statistics
by: Hemmer, Christoph Jürgen, et al.
Published: (2025)

Data-driven model order reduction for structures with piecewise linear nonlinearity using dynamic mode decomposition
by: Saito, Akira, et al.
Published: (2026)

Deficiency of equation-finding approach to data-driven modeling of dynamical systems
by: Zhai, Zheng-Meng, et al.
Published: (2025)

Data-driven forced response analysis with min-max representations of nonlinear restoring forces
by: Saito, Akira, et al.
Published: (2026)

Complex fractal trainability boundary can arise from trivial non-convexity
by: Liu, Yizhou
Published: (2024)

Data-Driven Spectral Analysis Through Pseudo-Resolvent Koopman Operator in Dynamical Systems
by: Xu, Yuanchao, et al.
Published: (2025)

Chaotic Dynamics and Fractal Geometry in Ring Lattice Systems of Nonchaotic Rulkov Neurons
by: Le, Brandon B.
Published: (2024)

Computing Chaotic Time-Averages from Few Periodic or Non-Periodic Orbits
by: Pughe-Sanford, Joshua L., et al.
Published: (2023)

Nonlinear optimals and their role in sustaining turbulence in channel flow
by: Klingenberg, Dario, et al.
Published: (2025)

Streams and Graphs of Dynamical Systems
by: De Leo, Roberto, et al.
Published: (2024)

Physics-Guided Actor-Critic Reinforcement Learning for Swimming in Turbulence
by: Koh, Christopher, et al.
Published: (2024)

Learning Beyond Experience: Generalizing to Unseen State Space with Reservoir Computing
by: Norton, Declan A., et al.
Published: (2025)

Deep Learning for Prediction and Classifying the Dynamical behaviour of Piecewise Smooth Maps
by: S, Vismaya V, et al.
Published: (2024)

Data-Driven Linearization of Dynamical Systems
by: Haller, George, et al.
Published: (2024)

Exploring Geometrical Properties of Chaotic Systems Through an Analysis of the Rulkov Neuron Maps
by: Le, Brandon B., et al.
Published: (2024)