论文标题
从数据中学习动态系统：一个简单的交叉验证角度
Learning dynamical systems from data: a simple cross-validation perspective
论文作者
论文摘要
从有限数量的观测状态中回归动力系统的向量场是学习此类系统的替代模型的自然方法。我们介绍了交叉验证的变体（基于最大平均差异和Lyapunov指数）的变体（cite \ cite \ cite {owhadi19}及其变体作为学习这些模拟器中使用的内核的简单方法。
Regressing the vector field of a dynamical system from a finite number of observed states is a natural way to learn surrogate models for such systems. We present variants of cross-validation (Kernel Flows \cite{Owhadi19} and its variants based on Maximum Mean Discrepancy and Lyapunov exponents) as simple approaches for learning the kernel used in these emulators.
