Overview

Chaotic dynamical systems are deterministic systems in which small differences in initial conditions can quickly separate trajectories, even when long-term statistical patterns remain stable. Researchers study attractors, Lyapunov exponents, bifurcations, and how these systems can be predicted, controlled, or inferred from limited noisy observations. Common approaches include equation discovery with sparse regression and Bayesian methods, neural ODEs, reservoir and Koopman models, and physics-informed learning. A central challenge is that accurate short-term forecasts do not guarantee faithful long-term dynamics: models must also reproduce invariant statistics, instability, and rare events.

Latest papers

CardsList

47 papers7 papers in the last four weeks, up 133% on the four weeks before. 0.0% of all new papers.

  1. Self-Organization from Constrained Geometric Radiation

    Oct 7, 2026Ming LeiChaotic Dynamical Systems

  2. Lyapunov-Inspired LyRIC Activation and GLARE Attention in Chaos-Guided State Space Modeling for EMG-To-Speech (ETS) Synthesis

    Oct 6, 2026Sajid Fardin Dipto, Tarikul Islam Tamiti, Luke Baja-Ricketts +2State Space ModelsChaotic Dynamical Systems

  3. A Response Theory Probe for Learned Stochastic AI Simulators, Tested on Lorenz-63

    Oct 5, 2026João Böger, Simon Driscoll, Niccolò Zagli +2Surrogate ModelingChaotic Dynamical Systems

  4. Do Neural PDE Solvers Learn the Right Dynamics?

    Oct 3, 2026Haonan Li, Yue Song, Bin Yang +1Neural PDE SolversDynamical Systems

  5. Physics is the Best Teacher: Consistency Learning for Time-Invariant Operators of Chaotic Dynamics

    Oct 2, 2026Lufang Chiang, Jiachen Yao, Thomas Y. L. Lin +1Chaotic Time-Series PredictionChaotic Dynamical Systems

  6. Learning Chaos Without Seeing Chaos: Extrapolation of Global Dynamics in Autoregressive Transformers

    Sep 30, 2026Yilun Liu, Yi Zhang, Ganyu Wu +5Dynamical SystemsChaotic Dynamical Systems

  7. Massively Parallel Reinforcement Learning with a Chaotic Reconfigurable Clockless Chip

    Sep 28, 2026Eric Oliveira-Gomes, Damien RontaniReinforcement LearningGPU-Accelerated RL

  8. Mechanism-Aware Ensemble Conditioning for Data-Limited Emulation of Extreme Events

    Sep 25, 2026Isabella S. Thiel, Juan Bello-Rivas, Yannis G. Kevrekidis +1Rare-Event EstimationChaotic Time-Series Prediction

  9. Nonequilibrium Phases of Repulsive Self-Attention: Chaos, Attention Condensation, and Emergent Locality

    Sep 23, 2026Qucheng Gao, Zuyi Yang, Xiao ChenSelf-AttentionChaotic Dynamical Systems

  10. Basin Geometry and Reliable Recall of Dynamical Memories in Reservoir Computing

    Sep 1, 2026Ling-Wei Kong, Ying-Cheng LaiDynamical SystemsReservoir Computing

  11. Sparse Koopman Autoencoders Identify Local Dynamical Regimes in Multibasin Systems

    Aug 29, 2026Aidan Li, Uday Kiran Reddy Tadipatri, Mahan Fathi +2Koopman Operator LearningDynamical Systems

  12. Missing the Butterfly and Predicting the Past: Features or Bugs of Accurate AI Weather Models?

    Aug 26, 2026Pedram Hassanzadeh, Weidong Li, Y. Qiang Sun +4Weather ForecastingChaotic Time-Series Prediction

  13. Dynamics-Aware Weighting for Deep Learning Forecasts of Chaotic Systems

    Aug 23, 2026Zhou Fang, Gianmarco MengaldoChaotic Time-Series PredictionAdaptive Loss Weighting

  14. Noise-Induced Predictability Redistribution Across Forecast Horizons of Extreme Events in Chaotic Dynamics

    Aug 20, 2026Andrei Velichko, Viet-Thanh PhamRare-Event PredictionChaotic Time-Series Prediction

  15. Eigenanalysis framework for autoregressive neural emulators of multi-scale chaotic dynamics

    Aug 17, 2026Conrad Ainslie, Pedram Hassanzadeh, Michael W. Mahoney +1Neural Network StabilityNeural Surrogate Modeling

  16. Equation-Free Period-Aware Forecast-Error Contraction for Estimating Negative Largest Lyapunov Exponents from Short Trajectory Ensembles

    Aug 6, 2026Andrei Velichko, N'Gbo N'Gbo, Viet-Thanh PhamChaotic Dynamical SystemsLyapunov Exponent Estimation

  17. Extrapolating the emergence of Hamiltonian chaos with random-feature Hamiltonian neural networks

    Jul 31, 2026Jaesung ChoiNeural Surrogate ModelingDynamical Systems

  18. Physics-Informed Neural Networks for Discovering Periodic Orbits in the Gravitational Three-Body Problem

    Jul 26, 2026Nikolaos Kollias, Nikolaos MatzakosDynamical SystemsChaotic Dynamical Systems

  19. Natural Invariant Measures for Chaotic Game Dynamics: Finding Order in Chaos

    Jul 23, 2026Jakub Bielawski, Thiparat Chotibut, Fryderyk Falniowski +2Dynamical SystemsGame Theory

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

    Jun 30, 2026Max Kreider, John Harlim, Daning HuangKernel Ridge RegressionKernel Regression

  21. Scalar Representations of Neural Network Training Dynamics

    Jun 29, 2026Pedro Jiménez-González, Miguel C. Soriano, Lucas LacasaNeural Network OptimizationChaotic Dynamical Systems

  22. Solving Inverse Problems of Chaotic Systems with Bidirectional Conditional Flow Matching

    Jun 23, 2026Peiyan Hu, Jian Zhang, Jiashu Pan +6Simulation-Based InferenceConditional Flow Matching

  23. Unified Geometry-Guided ML-FTLE for Tracking Transient Chaos from Scalar Time Series

    Jun 5, 2026S. V. Manivelan, Andrei Velichko, I. ManimehanDynamical SystemsChaotic Dynamical Systems

  24. Learning Chaotic Dynamics through Second-Order Geometric Supervision

    Jun 1, 2026Shinhoo Kang, Hai V. Nguyen, Tan Bui-ThanhNonlinear System IdentificationChaotic Dynamical Systems

  25. The Dynamic-Probabilistic Consistency Gap in Chaotic Surrogate Modeling

    May 29, 2026Andre Herz, Matthijs Pals, Daniel Durstewitz +1Dynamical SystemsSurrogate Modeling

  26. On Distributional Reinforcement Learning in Chaotic Dynamical Systems

    May 28, 2026James Rudd-Jones, Mirco Musolesi, María Pérez-OrtizDistributional RLReinforcement Learning

  27. Model discovery for dynamical systems with complex-valued product units

    May 26, 2026Martin Brückmann, Babette Dellen, Uwe JaekelNonlinear System IdentificationComplex-Valued Neural Networks