论文标题
“下一代”储层计算：时间步变形式中动态方程的经验数据驱动的表达
`Next Generation' Reservoir Computing: an Empirical Data-Driven Expression of Dynamical Equations in Time-Stepping Form
论文作者
论文摘要
基于非线性矢量自动进程（NVAR）的下一代储层计算用于模拟简单的动态系统模型，并将其与诸如Euler和$ 2^\ text {nd} $ orde unge lunge-kutta等数值集成方案进行了比较。结果表明，NVAR模拟器可以解释为用于恢复产生数据的数值集成方案的数据驱动方法。还表明该方法可以扩展以直接从数据中产生高阶数值方案。进一步检查了训练集中噪声和时间稀疏性的影响，以衡量该方法在更现实的应用中的潜在用途。
Next generation reservoir computing based on nonlinear vector autoregression (NVAR) is applied to emulate simple dynamical system models and compared to numerical integration schemes such as Euler and the $2^\text{nd}$ order Runge-Kutta. It is shown that the NVAR emulator can be interpreted as a data-driven method used to recover the numerical integration scheme that produced the data. It is also shown that the approach can be extended to produce high-order numerical schemes directly from data. The impacts of the presence of noise and temporal sparsity in the training set is further examined to gauge the potential use of this method for more realistic applications.
