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.

本研究提出了一种多分支网络方法，用于预测由复杂和混沌行为特征的物理吸引子的动力学。我们引入了一种独特的神经网络架构，由径向基函数（RBF）层和注意机制组成，旨在有效捕捉吸引子时序演化中的非线性相互依赖关系。我们的结果展示了使用包含约 28 分钟活动的 36,700 个时间序列观察值的真实数据集进行的 100 次预测中吸引子轨迹的成功预测。为了进一步说明我们提出技术的性能，我们提供了全面的可视化，展示了吸引子的原始和预测行为，并对观察到的结果与估计结果进行了定量衡量。总的来说，这项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方面的潜力，同时在需要准确的短期预测能力的各个领域提供了实际应用。