揭示复杂性:复杂实验数据分析中的奇异值分解

Judith F. Stein, Aviad Frydman, Richard Berkovits
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引用次数: 0

摘要

分析具有多个参数的复杂实验数据具有挑战性。我们建议使用奇异值分解(SVD)作为有效的解决方案。通过实际实验数据分析证明,这种方法在理解复杂物理数据方面超越了传统方法。奇异值和向量区分并突出了各种物理机制和尺度,揭示了以前具有挑战性的元素。SVD 是导航复杂实验景观的强大工具,为各种实验测量带来了希望。
本文章由计算机程序翻译,如有差异,请以英文原文为准。
Unraveling Complexity: Singular Value Decomposition in Complex Experimental Data Analysis
Analyzing complex experimental data with multiple parameters is challenging. We propose using Singular Value Decomposition (SVD) as an effective solution. This method, demonstrated through real experimental data analysis, surpasses conventional approaches in understanding complex physics data. Singular values and vectors distinguish and highlight various physical mechanisms and scales, revealing previously challenging elements. SVD emerges as a powerful tool for navigating complex experimental landscapes, showing promise for diverse experimental measurements.
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