This repository contains the source code for the Logistic Map Simulator, an interactive tool designed to explore chaos theory, nonlinear dynamics, and predictability limits in 1D dynamical systems.
Unlike standard simulators, this app demonstrates the chaotic behavior of the one-dimensional logistic equation with a specific focus on model error and initial-condition uncertainty. It allows researchers and students to visualize how uncertainty propagates in chaotic models, leading to a "predictability horizon."
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Interactive Logistic Map: Adjust the growth parameter (
$r$ ) to observe bifurcation and the transition to chaos. - Predictability Analysis: Model the impact of Monte Carlo simulations on system variability.
- Chaos Visualization: Direct application of research regarding how model biases affect long-term forecasts.
This tool was developed by Dr. Altug Aksoy (University of Miami / CIMAS & NOAA / AOML) as a companion to the following research:
Aksoy, A. (2024). A Monte Carlo approach to understanding the impacts of initial-condition uncertainty, model uncertainty, and simulation variability on the predictability of chaotic systems. Perspectives from the one-dimensional logistic map. Chaos, 34, 011102. Access on Chaos
If you have questions regarding the research or encounter technical issues with the simulator, please feel free to reach out:
- Direct Inquiry: Click here to send an email inquiry
- Community: Join the conversation in the Discussions tab!