  We present a taxonomy of research on Machine Learning (ML) applied to enhance
simulations together with a catalog of some activities. We cover eight patterns
for the link of ML to the simulations or systems plus three algorithmic areas:
particle dynamics, agent-based models and partial differential equations. The
patterns are further divided into three action areas: Improving simulation with
Configurations and Integration of Data, Learn Structure, Theory and Model for
Simulation, and Learn to make Surrogates.
