论文标题
统计和机器学习方法，用于预测长期激发能量转移动力学
Statistical and machine learning approaches for prediction of long-time excitation energy transfer dynamics
论文作者
论文摘要
用于解决开放量子系统动力学的方法之一是运动的层次方程（HEOM）。尽管它在数值上是确切的，但此方法需要巨大的计算资源才能解决。这里的目的是证明诸如Sarima，Catboost，Prophet，卷积和经常性神经网络等模型是否能够绕过这一要求。我们能够通过首先求解HEOM来成功地展示这一点，以生成一个数据集的时间序列，该数据集描述了光合系统中激发能量传递的耗散动力学，然后我们使用此数据来测试模型在仅给出初始短时动力学时预测长时间动态的模型。我们的结果表明，Sarima模型可以作为预测长期动力学的计算便宜而准确的方法。
One of the approaches used to solve for the dynamics of open quantum systems is the hierarchical equations of motion (HEOM). Although it is numerically exact, this method requires immense computational resources to solve. The objective here is to demonstrate whether models such as SARIMA, CatBoost, Prophet, convolutional and recurrent neural networks are able to bypass this requirement. We are able to show this successfully by first solving the HEOM to generate a data set of time series that depict the dissipative dynamics of excitation energy transfer in photosynthetic systems then, we use this data to test the models ability to predict the long-time dynamics when only the initial short-time dynamics is given. Our results suggest that the SARIMA model can serve as a computationally inexpensive yet accurate way to predict long-time dynamics.
