Effective theory building and manifold learning
Bibliographic Data
| ID | 10813823 |
|---|---|
| Authors | David Peter Wallis Freeborn (0000-0002-2117-8145, Eastern University, corresponding author) |
| Year | 2025 |
| Volume | 205 |
| Issue | 1 |
| Publication date | 2025-01-07 |
| Open Access | Yes |
| Type | ARTICLE |
| Venue | Synthese (JOURNAL) |
| Journal identifiers | ISSN: 0039-7857 • E-ISSN: 1573-0964 |
| Publisher | Springer Science and Business Media LLC (PUBLISHER) |
| DOI | 10.1007/s11229-024-04844-0 |
| OpenAlex | W4406149328 |
| Language | EN |
| References cited | 58 |
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
Construct (python library) · Epistemology · Field (mathematics) · Manifold (fluid mechanics) · Metaphysics · Philosophy of language · Philosophy of science · Physics · Pure mathematics · Theoretical physics · Artificial Intelligence · Computer Science · Engineering · Mathematics · Philosophy · Protein Structure and Dynamics · Theoretical Computer Science
The Structure of Science
Regularization and Variable Selection Via the Elastic Net
Umap
Statistical Modeling
Representation Learning
Reducing the Dimensionality of Data with Neural Networks
Gradient-based learning applied to document recognition
Principal component analysis
The Use of Multiple Measurements in Taxonomic Problems
Deep learning
Who’s Afraid of Nagelian Reduction?
Sloppy Models, Renormalization Group Realism, and the Success of Science
Critical phenomena and breaking drops
In Defence of Naiveté
Effective theories and infinite idealizations
Approaches to Reduction
Renormalization and the Formulation of Scientific Realism
Renormalization Group Realism
Scientific Realism Made Effective
The Real Problem with Perturbative Quantum Field Theory
Understanding from Machine Learning Models
| Highly cited | No |
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