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.

翻译：本研究提出一种多分支网络方法，用于预测具有复杂混沌行为的物理吸引子动力学。我们引入一种独特的神经网络架构，该架构由径向基函数层与注意力机制结合构成，旨在有效捕捉吸引子时间演化中固有的非线性相互依赖关系。实验结果表明，基于包含约28分钟活动时长、共36,700个时间序列观测值的真实数据集，该方法成功实现了对吸引子轨迹的100步预测。为进一步说明所提技术的性能，我们提供了展示吸引子原始行为与预测行为的综合可视化图表，并辅以观测结果与估计结果的定量比较指标。总体而言，本工作展示了先进机器学习算法在揭示复杂物理系统中隐藏结构的潜力，同时为需要精准短期预测能力的各领域提供了实际应用方案。