A Gaussian process (GP)-based methodology is proposed to emulate computationally expensive dynamical computer models or simulators. The method relies on emulating the short-time numerical flow map of the model. The flow map returns the solution of a dynamic system at an arbitrary time for a given initial condition. The prediction of the flow map is performed via a GP whose kernel is estimated using random Fourier features. This gives a distribution over the flow map such that each realisation serves as an approximation to the flow map. A realisation is then employed in an iterative manner to perform one-step ahead predictions and forecast the whole time series. Repeating this procedure with multiple draws from the emulated flow map provides a probability distribution over the time series. The mean and variance of that distribution are used as the model output prediction and a measure of the associated uncertainty, respectively. The proposed method is used to emulate several dynamic non-linear simulators including the well-known Lorenz attractor and van der Pol oscillator. The results show that our approach has a high prediction performance in emulating such systems with an accurate representation of the prediction uncertainty.
翻译:以 Gausian 进程为基础的方法( GP) 提议模仿计算成本昂贵的动态计算机模型或模拟器。 方法依赖于模拟模型的短时数字流图。 流程图在特定初始条件的任意时间返回动态系统的解决方案。 流程图的预测通过使用随机的 Fourier 特征进行, 其内核估计的 GP 流程图的预测。 这样在流程图上进行分布, 使每个实现都能够接近流程图。 然后, 以迭接方式实现实现, 来进行前一步的预测并预报整个时间序列。 从模拟流程图中多次抽取的重复此程序提供了时间序列的概率分布。 该分布的平均值和差异分别用作模型输出预测和相关不确定性的测量。 拟议方法用来模仿几个动态的非线性模拟器, 包括著名的Lorenz 吸引器和 van der Pol 振测器。 结果显示, 我们的方法在模拟这种系统时具有高预测性, 并准确表示不确定性。