# Effective Theory Building and Manifold Learning

**Authors:** David Peter Wallis Freeborn  
**arXiv:** [2411.15975](https://arxiv.org/abs/2411.15975)  
**Date:** 2024-11-24  
**Summary page:** https://www.summarizepaper.com/fr/paper/2411.15975v1/

## Abstract

Manifold learning and effective model building are generally viewed as fundamentally different types of procedure. After all, in one we build a simplified model of the data, in the other, we construct a simplified model of the another model. Nonetheless, I argue that certain kinds of high-dimensional effective model building, and effective field theory construction in quantum field theory, can be viewed as special cases of manifold learning. I argue that this helps to shed light on all of these techniques. First, it suggests that the effective model building procedure depends upon a certain kind of algorithmic compressibility requirement. All three approaches assume that real-world systems exhibit certain redundancies, due to regularities. The use of these regularities to build simplified models is essential for scientific progress in many different domains.