Title
Testing machine learning at classical electrodynamics
Date Issued
08 September 2021
Access level
metadata only access
Resource Type
conference paper
Publisher(s)
Institute of Electrical and Electronics Engineers Inc.
Abstract
Like physics or another laws-based basic science, machine learning might also be a firm methodology to solve physics problems by the which a kind of optimization and minimization of energy are needed. Expressed at the Mitchell's principles, machine learning can be seen as a strategy that allows to improve physical actions such as observation and measurement. In the classical territory, one can project the well-known electrodynamics over the steps: (i) task, (ii) performance, and (iii) experience. With this one might to guarantee a kind of learning to face a next similar situation and so on. This paper try to solve the problem of a charged particle inside a cylindrical volume but emphasizing its energy and its measurement. Simulations have shown that machine learning can also be an alternative tool to solve physics problems that require of minimization of energy.
Language
English
OCDE Knowledge area
Electroquímica
Ingeniería eléctrica, Ingeniería electrónica
Scopus EID
2-s2.0-85118449716
ISBN of the container
9789532901122
Conference
2021 6th International Conference on Smart and Sustainable Technologies, SpliTech 2021
Sources of information:
Directorio de Producción Científica
Scopus