Freeborn, David Peter Wallis (2024) Effective Theory Building and Manifold Learning. [Preprint]

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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.

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Item Type:	Preprint
Creators:	Creators Email ORCID Freeborn, David Peter Wallis dfreebor@uci.edu 0000-0002-2117-8145
Keywords:	manifold learning, renormalization, effective theories, effective models, renormalization group, renormalization group realism, effective realism, sloppy models, models, quantum field theory, philosophy of physics, philosophy of modelling, philosophy of science, QFTs, EFTs, manifolds, computer science, philosophy of artificial intelligence, artificial intelligence, machine learning, epistemology of models, computational models, reduction, reduction and emergence, philosophy of computer science, philosophy of computation,
Subjects:	General Issues > Data Specific Sciences > Computation/Information Specific Sciences > Artificial Intelligence General Issues > Computer Simulation Specific Sciences > Physics > Condensed Matter Specific Sciences > Physics > Fields and Particles Specific Sciences > Artificial Intelligence > Machine Learning General Issues > Models and Idealization Specific Sciences > Physics Specific Sciences > Probability/Statistics Specific Sciences > Physics > Quantum Field Theory General Issues > Realism/Anti-realism Specific Sciences > Physics > Statistical Mechanics/Thermodynamics General Issues > Structure of Theories General Issues > Theory Change
Depositing User:	David Freeborn
Date Deposited:	26 Nov 2024 13:57
Last Modified:	26 Nov 2024 13:57
Item ID:	24257
Subjects:	General Issues > Data Specific Sciences > Computation/Information Specific Sciences > Artificial Intelligence General Issues > Computer Simulation Specific Sciences > Physics > Condensed Matter Specific Sciences > Physics > Fields and Particles Specific Sciences > Artificial Intelligence > Machine Learning General Issues > Models and Idealization Specific Sciences > Physics Specific Sciences > Probability/Statistics Specific Sciences > Physics > Quantum Field Theory General Issues > Realism/Anti-realism Specific Sciences > Physics > Statistical Mechanics/Thermodynamics General Issues > Structure of Theories General Issues > Theory Change
Date:	22 November 2024
URI:	https://philsci-archive.pitt.edu/id/eprint/24257

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• Effective Theory Building and Manifold Learning. (deposited 22 Nov 2024 13:27)
  • Effective Theory Building and Manifold Learning. (deposited 26 Nov 2024 13:57) [Currently Displayed]

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