Reinforcement Learning for the Optimization of Explicit Runge-Kutta Method Parameters
Reinforcement learning is one of the three main paradigms in machine learning, which is increasingly used as a method to approach scientific problems. In this thesis, we introduce and use reinforcement learning to find the optimal parameters of a numerical solver. We first motivate that solving the linear systems can be done by solving initial value problems. These initial values problems can the