论文标题
神经网络代表时间集成商
Neural Network Representation of Time Integrators
论文作者
论文摘要
构建了深层神经网络（DNN）体系结构，这些结构与数值时间集成的显式runge-kutta方案相当。给出了网络权重和偏见，即不需要培训。这样，基于物理的集成器留下的唯一任务是右侧的DNN近似。这允许清楚地描述右侧错误和时间集成错误的近似估计。以简单的大规模抑制态度案例集成所需的架构作为示例。
Deep neural network (DNN) architectures are constructed that are the exact equivalent of explicit Runge-Kutta schemes for numerical time integration. The network weights and biases are given, i.e., no training is needed. In this way, the only task left for physics-based integrators is the DNN approximation of the right-hand side. This allows to clearly delineate the approximation estimates for right-hand side errors and time integration errors. The architecture required for the integration of a simple mass-damper-stiffness case is included as an example.
