Explicit Application of Artificial Neural Networks for Quantization of Energy Without Quantum Mechanics

Nieto-Chaupis H. · 2025 IEEE 16th Annual Ubiquitous Computing, Electronics and Mobile Communication Conference, UEMCON 2025 (2025) · DOI: 10.1109/uemcon67449.2025.11267600

Resumen

Normally, one employs quantum mechanics to derive energy of system. Inside the quantum framework, it is demanded to usage the theoretical machinery that demands some expertise in various branches of advanced mathematics. In this manner, one can wonder if in the case of complex systems that would demand to use advanced algorithms and numerical methods, the support of machine learning might simplify time consumption and computer resources? The purpose of this paper is just to answer this question through the usage artificial neural network to calculate the simple physics scenario constituted by a quantum harmonic oscillator. Therefore, it is demonstrated that by using a simple perceptron the system energies can be extracted in a straightforward manner. Extensions of presented applied methodology are also presented.

Instituciones: Universidad Autónoma del Perú