As insufficient data volume and quality remain the key impediments to the adoption of modern subsymbolic AI, techniques of synthetic data generation are in high demand. Simulation offers an apt, systematic approach to generating diverse synthetic data. This chapter introduces the reader to the key concepts, benefits, and challenges of simulation-based synthetic data generation for AI training purposes, and to a reference framework to describe, design, and analyze digital twin-based AI simulation solutions.

翻译：由于数据量不足与质量欠佳仍是制约现代亚符号人工智能应用的主要障碍，合成数据生成技术备受关注。仿真为生成多样化合成数据提供了一种恰当且系统化的方法。本章向读者介绍基于仿真的合成数据生成在AI训练中的核心概念、优势与挑战，并提供一个参考框架用以描述、设计和分析基于数字孪生的AI仿真解决方案。