  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.
