Forecasting unknown dynamics is of great interest across many physics-related disciplines. However, data-driven machine learning methods are bothered by the poor generalization issue. To this end, a forecasting model based on symbolic invariance (i.e., expressions/equations that represent intrinsic system mechanisms) proposed. By training and pruning neural network wrapped in numerical integrat...