Machine Learning and Statistical Methods for Studying Voids and Photothermal Effects of a Semiconductor Rotational Medium with Thermal Relaxation Time

Abstract

Machine learning is the process of creating algorithms that extract useful facts from data automatically. The goal of this paper is to use an artificial neural network and a cubic spline model to predict various physical quantities displacement components in a thermoplastic solid, such as elastic waves, vector form, volume fraction field, thermal waves, stress components, and carrier density concentration (plasma waves). The mean absolute scaled error (MASE), the mean absolute percentage error (MAPE), and the symmetric mean absolute percentage errors (SMAPE) are used to compare the accuracy of two models. The true displacements are given their maximum expected values. These factors have also been described using various descriptive statistics and diagrams. Statistical significance was found in the examination of the correlation between the variables, and a comparison was conducted between the findings and prior results acquired by others. The findings show that voids, rotation, optical temperature, and thermal relaxation all have a significant impact on the phenomena, and they are in line with earlier physical findings. Furthermore, it is demonstrated that certain physical variables describing such systems may display this property, allowing for the development of an analytical criterion for the advent of dynamical chaos.

Photothermal Radiometry Data Analysis by Using Machine Learning

“…Their model was based on an original deep unfolding neural network (USRNet) [22]. Jawa et al have used machine learning and statistical methods for studying voids and photothermal effects of a semiconductor rotational medium with thermal relaxation time [23]. Kovács et al [24] have investigated deep learning approaches, based on U-net [25], for recovering initial temperature profiles from thermographic images in non-destructive material testing.…”

Semiconductor elastic medium with electromagnetic porosity, photothermal excitation, and the Thomson effect

“…Khalil et al [13] studied electromagnetic field and initial stress on a photothermal semiconducting void medium under thermoelasticity theories. Jawa et al [14] produced machine learning and statistical methods for studying voids and photothermal effects of a semiconductor rotational medium with thermal relaxation time.…”