A new method of numerical integration is presented here, the variable-step Störmer-Cowell method. The method uses error control to regulate the step size, so larger step sizes can be taken when possible, and is double-integration, so only one evaluation per step is necessary when integrating second-order differential equations. The method is not variable-order, because variable-order algorithms...

The usefulness and value of Multi-step Machine Learning (ML), where a task is organized into connected sub-tasks with known intermediate inference goals, as opposed to single large model learned end-to-end without sub-tasks, presented. Pre-optimized ML models are better performance obtained by re-optimizing the one. selection an from several small candidates for each sub-task has been performed...