A Multi-Branched Radial Basis Network Approach to Predicting Complex
  Chaotic Behaviours
Aarush Sinha
In this study, we propose a multi branched network approach to predict the
dynamics of a physics attractor characterized by intricate and chaotic
behavior. We introduce a unique neural network architecture comprised of Radial
Basis Function (RBF) layers combined with an attention mechanism designed to
effectively capture nonlinear inter-dependencies inherent in the attractor's
temporal evolution. Our results demonstrate successful prediction of the
attractor's trajectory across 100 predictions made using a real-world dataset
of 36,700 time-series observations encompassing approximately 28 minutes of
activity. To further illustrate the performance of our proposed technique, we
provide comprehensive visualizations depicting the attractor's original and
predicted behaviors alongside quantitative measures comparing observed versus
estimated outcomes. Overall, this work showcases the potential of advanced
machine learning algorithms in elucidating hidden structures in complex
physical systems while offering practical applications in various domains
requiring accurate short-term forecasting capabilities.