Chaotic Dynamical Systems
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
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