基本信息
- 来源: arxiv
- 原始来源: https://arxiv.org/abs/2602.15816v1
- 作者: Xiaoran Liu, Istvan David
- 分类: cs.AI
- 论文时间: 2026-02-17T18:53:27Z
- 论文 PDF: https://arxiv.org/pdf/2602.15816v1.pdf
来源摘要/节选
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.
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