We discuss a software package for incorporating into simulations data-driven models trained using machine learning methods. These can be used for (i) modeling dynamics and time-step integration, (ii) modeling interactions between system components, and (iii) computing quantities of interest characterizing system state. The package allows for use of machine learning methods with general model classes including Neural Networks, Gaussian Process Regression, Kernel Models, and other approaches. We discuss in this whitepaper our prototype C++ package, aims, and example usage.

翻译：我们讨论了将利用机器学习方法培训的数据驱动模型纳入模拟模型的软件包,这些软件包可用于(一) 建模动态和时间步骤整合,(二) 系统各组成部分之间的建模互动,(三) 计算系统特点化的利息量。软件包允许使用机器学习方法,包括神经网络、高山进程回归、内核模型和其他方法。我们在本白皮书中讨论了原型的C++组合、目标和示例使用。