Learning Everywhere：一个集成机器学习和仿真的分类法
Learning Everywhere: A Taxonomy for the Integration of Machine Learning and Simulations
摘要：
我们提出了一个分类的研究机器学习（ML）应用于增强模拟与一些活动的目录。我们涵盖了ML与模拟或系统的链接的八种模式，以及三个算法领域：粒子动力学，基于代理的模型和偏微分方程。这些模式进一步分为三个行动领域：通过数据配置和集成改进模拟，学习模拟的结构、理论和模型，以及学习制作代理。
英文摘要：
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.
机构、总结、中文摘要均由AI分析而来，可能存在错误，请以论文正文为准。
