Here, we have employed principal component analysis (PCA) and linear discriminant analysis (LDA) to analyze the Mie-calculated UV-Vis spectra of gold nanospheres (GNS). Eigen spectra of PCA perform the Fano-type resonances. PCA vector spectra determine the 3D vector fields which reveal the homoclinic orbit strange attractor. Quantum confinement effects are observed by the 3D representation of LDA. Standing wave patterns resulting from oscillations of ion-acoustic phonon and electron waves are illustrated through the eigen spectra of LDA. Such capabilities of GNPs have brought high attention to the high energy density physics applications. Furthermore, accurate prediction of gold nanoparticle (GNP) sizes using machine learning could provide rapid analysis without the need for expensive analysis. Two hybrid algorithms consist of unsupervised PCA and two different supervised ANN have been used to estimate the diameters of GNPs. PCA-based artificial neural networks(ANN) were found to estimate the diameters with a high accuracy.

在这里，我们采用了主成分分析（PCA）和线性判别分析（LDA）来分析Mie计算的金纳米球（GNS）的UV-Vis光谱。PCA的本征光谱执行Fano型共振。PCA矢量光谱确定3D矢量场，这些场揭示了同斜轨道奇异吸引子。通过LDA的3D表示可以观察到量子限制效应。通过LDA的本征光谱说明了离子声子和电子波的振荡所产生的驻波模式。GNP的这种功能引起了对高能量密度物理应用的高度关注。此外，使用机器学习对金纳米颗粒（GNP）尺寸的准确预测可以提供快速分析，而无需进行昂贵的分析。两种混合算法由无监督的PCA和两种不同的监督的ANN组成，用于估计GNP的直径。发现基于PCA的人工神经网络（ANN）可以高精度估计直径。