Description

Symbolic regression is used to discover mathematical expressions of functions that can fit the given data based on the rules of accuracy, simplicity, and generalization. Without any prior knowledge of physics, kinematics, and geometry, some natural laws described by mathematical expressions, such as Hamiltonians, Lagrangians, and other laws of geometric and momentum conservation, can be inferred from experimental data by symbolic regression approaches.

The aim of this project is to investigate the use of available libraries for symbolic regression and assess their usefulness to identify and characterise missing components of dynamical equations.