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.
@article{arxiv.1909.13340,
title = {Learning Everywhere: A Taxonomy for the Integration of Machine Learning and Simulations},
author = {Geoffrey Fox and Shantenu Jha},
journal= {arXiv preprint arXiv:1909.13340},
year = {2019}
}
Comments
15th International Conference eScience 2019, September 24-27, 2019, San Diego, California,