Document Type
Honors Thesis
Abstract
At a particular point during the scientific process, we need to understand the relationships between different variables. In science, generally, the relationships are derived using the first principle. Developing another way to determine the relationship among variables of a physical system will help to boost not only overall scientific research but may also aid greatly in the scientific discovery process. Symbolic regression, which is a process of discovering a symbolic expression to describe the given data, can be used to determine relationships between variables. However, the problem with symbolic regression is that we have to go through many combinations before the relationship is discovered. In this project, we aim to discuss the overall progress, with more focus on the current use of Artificial Intelligence (AI) in this field, one of which is the AI-Feynman package. Here, we will also discuss in-depth about various experimentation conducted with this package.
Publication Date
12-1-2022
Language
English
License
This work is licensed under a Creative Commons Attribution-NonCommercial-Share Alike 4.0 International License.
Recommended Citation
Sapkota, Parvat, "STUDY OF THE AI-FEYNMAN PACKAGE" (2022). 2022 Fall Honors Capstone Projects. 12.
https://mavmatrix.uta.edu/honors_fall2022/12