论文标题
朝向完全物理信息的回声状态网络 - 基于经常性人工神经元的ode近似器
Toward the Fully Physics-Informed Echo State Network -- an ODE Approximator Based on Recurrent Artificial Neurons
论文作者
论文摘要
受到最新理论论点的启发，对物理信息的回声状态网络（ESN）进行了讨论，该尝试以物理信息的方式绝对培训储层模型。由于在这样的目的上最明确的工作，ode（普通微分方程）近似值旨在根据复发评估复制解决方案。在微分方程的主要不变性上，复发的约束只是为了确保基于ESN的ODE近似器的适当回归方法。在此之后，建立了关于两次通行策略回归策略的想法的实际培训过程。针对完全物理知识的储层模型，证明了几个非线性动力学问题是从本研究中提出的方法中获得的计算。
Inspired by recent theoretical arguments, physics-informed echo state network (ESN) is discussed on the attempt to train a reservoir model absolutely in physics-informed manner. As the plainest work on such a purpose, an ODE (ordinary differential equation) approximator is designed to replicate the solution in sequence with respect to the recurrent evaluations. On the principal invariance of differential equations, the constraint in recurrence just takes shape to secure a proper regression method for the ESN-based ODE approximator. After then, the actual training process is established on the idea of two-pass strategy for regression. Aiming at the fully physics-informed reservoir model, a couple of nonlinear dynamical problems are demonstrated as the computations obtained from the proposed method in this study.
