Analysis of Phonetic Soliton Propagation in Neutral Weyl Fermion-sea

TL;DR

This paper explores the use of machine learning to analyze ultrasonic nonlinear elastic wave spectroscopy in a complex quantum system modeled by Weyl fermions, employing lattice simulations and Clifford algebra transformations.

Contribution

It introduces a novel approach combining ML with lattice simulation of Weyl fermion systems to optimize topological features in nonlinear elastic wave analysis.

Findings

ML effectively identifies optimal lattice action weights.

Clifford Fourier Transform enables analysis in real space.

Application demonstrates potential in quantum wave propagation analysis.

Abstract

We propose application of Machine Learning (ML) and Neural Network (NN) technique for the analysis of ultrasonic Time Reversal based Nonlinear Elastic Wave Spectroscopy (TR-NEWS). In order to acquire topological features, we adopt the $(2+1)D$ lattice simulation with fixed point (FP) actions. We consider 7 A type loops which sit on $2D$ spacial plane spanned by $e_1, e_2$ and 13 B type loops which contain links parallel to $e_1\wedge e_2$ and to -$e_1\wedge e_2$. We consider propagation of bosonic phonons in Fermi-sea of neutral Weyl spinors which are described by Clifford algebra. Configurations in momentum space is transformed to real position space via Clifford Fourier Transform. We consider A-type without hysteresis effects and B-type with hysteresis effects, and via ML or NN technique search optimal weight of 7 A-type FP actions and 13 B-type FP actions using the Monte-Carlo method.