arXiv:2605.30453hep-phcs.LG2026-05被引 3
解析生成模型在物理中的应用与评估难题
Generative Models and Statistical Validation
- 梳理现代生成网络的基本框架
- 提出评估生成模型精度与统计效能的方法论挑战
- 适合关注生成模型可信度的物理与数据科学家
生成式机器学习已成为理论与实验物理学中不可或缺的工具，尤其在快速替代模型和概率密度估计方面。本文首先介绍现代生成网络的底层框架，随后探讨如何量化其准确性、精确性及统计功效所面临的挑战。
原文摘要 · Abstract (English)
Generative machine learning has become an essential tool in theoretical and experimental physics, especially in the context of fast surrogates and density estimators. In this work, we first introduce the underlying framework of modern generative networks and then discuss challenges in quantifying their accuracy, precision, and statistical power.
生成模型统计验证物理计算
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