# Experimental modeling of physical laws

## TL;DR

This paper presents a method for modeling physical laws by estimating probability distributions of measured variables using kernel estimators, optimizing data redundancy and estimation error through a creation-annihilation process.

## Contribution

It introduces a novel approach combining kernel density estimation with an information entropy framework and a creation-annihilation optimization process for physical law modeling.

## Key findings

- Effective modeling of physical laws from experimental data.
- Reduction of estimation error and data redundancy.
- Application of the creation-annihilation process for optimization.

## Abstract

A physical law is represented by the probability distribution of a measured variable. The probability density is described by measured data using an estimator whose kernel is the instrument scattering function. The experimental information and data redundancy are defined in terms of information entropy. The model cost function, comprised of data redundancy and estimation error, is minimized by the creation-annihilation process.