English(EN) Flexible Spectral-Normalized Neural Gaussian Process for Dynamic Aperture Prediction

新的机器学习方法加速粒子加速器的不确定性量化

作者 PulseAugur 编辑部 · [1 个来源] · 2026-09-10 04:00

研究人员开发了一种新颖的经验贝叶斯方法，用于灵活、异方差谱归一化神经高斯过程的超参数进行高效调优。该技术显著降低了复杂机器学习模型相关的计算成本，使其更适用于大规模科学应用。该方法已成功应用于粒子加速器的动态孔径预测，特别是针对CERN的大型强子对撞机，为传统的计算密集型方法提供了一种更具成本效益的替代方案，同时保持了具有竞争力的性能和准确的不确定性估计。 AI

影响该方法可以实现对复杂科学模拟中更有效的不确定性量化，降低粒子物理学等领域研究的计算成本。

排序理由详细介绍新机器学习方法的学术论文。[lever_c_demoted from research: ic=1 ai=1.0]

在 arXiv cs.LG 阅读 →

AI 生成摘要 · Google Gemini · 来自 1 个来源。我们如何撰写摘要 →

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报道来源 [1]

arXiv cs.LG TIER_1 English(EN) · Yousra El-Bachir, Frederik Van der Veken, Davide di Croce, Carlo Emilio Montanari, Massimo Giovannozzi, Ekaterina Krymova, Tatiana Pieloni · 2026-09-10 04:00

用于动态孔径预测的柔性谱归一化神经高斯过程

arXiv:2609.08620v1 Announce Type: cross 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 si…

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报道来源 [1]

用于动态孔径预测的柔性谱归一化神经高斯过程

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