Generative Models and Statistical Validation

2026-05-28

AI Summary

This work addresses the pressing need for reliable statistical validation frameworks in the application of generative models to physical sciences. It proposes a systematic evaluation methodology that integrates modern generative neural networks, density estimation, and surrogate modeling techniques to quantitatively assess model performance across three key dimensions: accuracy, precision, and statistical power. For the first time, this study establishes rigorous criteria for evaluating the credibility of generative models in scientific computing, elucidates their current limitations, and lays both theoretical and practical foundations for developing high-fidelity physics-informed surrogate models.

Abstract

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.

Problem

• Generative Models
• Statistical Validation
• Accuracy
• Precision
• Statistical Power

Innovation

• generative models
• statistical validation
• density estimation
• surrogate modeling
• statistical power