A Fourier-Galerkin method with explicit error bounds rigorously computes stationary measures and Lyapunov exponents, proving noise-induced order transitions in Gaussian-noise perturbed unimodal maps.
Efficient computation of stationary measures and the Lyapunov Landscape for families random dynamical systems with smooth additive noise
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Reliable Programmatic Weak Supervision with Confidence Intervals for Label Probabilities
A Fourier-Galerkin method with explicit error bounds rigorously computes stationary measures and Lyapunov exponents, proving noise-induced order transitions in Gaussian-noise perturbed unimodal maps.