We consider initial value problems of nonlinear dynamical systems, which include physical parameters. A quantity of interest depending on the solution is observed. A discretisation yields the trajectories of the quantity of interest in many time points. We examine the mapping from the set of parameters to the discrete values of the trajectories. An evaluation of this mapping requires to solve an initial value problem. Alternatively, we determine an approximation, where the evaluation requires low computation work, using a concept of machine learning. We employ feedforward neural networks, which are fitted to data from samples of the trajectories. Results of numerical computations are presented for a test example modelling an electric circuit.

翻译：我们考虑的是非线性动态系统的初步价值问题,其中包括物理参数。观测到一个取决于解决方案的利息数量。一个离散可以产生许多时间点的利息数量的轨迹。我们检查从一组参数到轨道的离散值的绘图。对这个绘图的评估需要解决最初的价值问题。或者,我们确定一个近似值,因为评价需要低计算工作,使用机器学习的概念。我们使用进料前神经网络,这些网络与轨迹样本的数据相匹配。数字计算的结果被展示给一个模拟电路的试验示例。