Flexible Spectral-Normalized Neural Gaussian Process for Dynamic Aperture Prediction

2026-09-08

AI Summary

本文提出了一种用于动态孔径预测的谱归一化神经高斯过程方法，通过集成超参数学习减少计算负担，解决大规模科学应用中的不确定性量化问题。

Abstract

We address the challenge of scalable uncertainty quantification in large-scale scientific applications, where complex state-of-the-art machine learning methods are often computationally infeasible. Our primary contribution is a simple yet effective empirical Bayes method for automatically tuning the hyperparameters of a flexible, heteroscedastic Spectral-normalized Neural Gaussian Process. This approach retains the expressiveness and uncertainty-awareness of semi-Bayesian neural models while significantly reducing the computational burden by integrating hyperparameter learning directly into the training loop. We demonstrate the practical impact of our method on the task of estimating the dynamic aperture in circular particle accelerators, a fundamental problem in high-energy physics colliders and storage rings, using simulation data from the case of the Large Hadron Collider at CERN. Traditional approaches to DA estimation require extensive particle-tracking simulations, which are prohibitively time-consuming and resource-intensive. Our results show that the proposed method achieves competitive predictive performance and well-calibrated uncertainty estimates at much lower computational cost than state-of-the-art approaches. We stress that, beyond this application, the proposed empirical Bayes framework offers a general solution for training heteroscedastic neural models in situations where manual hyperparameter tuning is impractical. Accordingly, we anticipate that this framework can be applied to other domains that encounter comparable computational limitations.

Problem

• uncertainty quantification
• large-scale scientific applications
• dynamic aperture
• computational burden
• heteroscedastic neural models

Innovation

• Empirical Bayes
• Spectral-normalized Neural Gaussian Process
• Heteroscedastic
• Automatic Hyperparameter Tuning
• Uncertainty Quantification