Effective Theory Building and Manifold Learning

Autor:	Freeborn, David Peter Wallis
Rok vydání:	2024
Předmět:	Physics - History and Philosophy of Physics High Energy Physics - Phenomenology High Energy Physics - Theory Mathematical Physics
Druh dokumentu:	Working Paper
Popis:	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. Comment: 33 pages
Databáze:	arXiv
Externí odkaz:	http://arxiv.org/abs/2411.15975 Zobrazit plný text záznamu View this record from Arxiv