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：在本研究中，我们提出了一种多分支网络方法，用于预测以复杂混沌行为为特征的物理吸引子的动力学特性。我们引入了一种独特的神经网络架构，该架构由径向基函数（RBF）层与注意力机制组合而成，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。我们的结果表明，基于包含约28分钟活动的36,700个时间序列观测值的真实世界数据集，该模型成功实现了对吸引子轨迹的100步预测。为进一步展示所提技术的性能，我们提供了全面的可视化结果，描绘了吸引子的原始行为与预测行为，并结合定量指标比较了观测值与估计值。总体而言，这项工作展示了先进机器学习算法在揭示复杂物理系统隐藏结构方面的潜力，同时为需要精确短期预测能力的各种领域提供了实际应用。