Effective Theory Building and Manifold Learning
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arXiv
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| Format: | Preprint |
| Published: |
2024
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| _version_ | 1866913584963911680 |
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| author | Freeborn, David Peter Wallis |
| author_facet | Freeborn, David Peter Wallis |
| contents | 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. |
| format | Preprint |
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arxiv_https___arxiv_org_abs_2411_15975 |
| institution | arXiv |
| publishDate | 2024 |
| record_format | arxiv |
| spellingShingle | Effective Theory Building and Manifold Learning Freeborn, David Peter Wallis History and Philosophy of Physics High Energy Physics - Phenomenology High Energy Physics - Theory Mathematical Physics 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. |
| title | Effective Theory Building and Manifold Learning |
| topic | History and Philosophy of Physics High Energy Physics - Phenomenology High Energy Physics - Theory Mathematical Physics |
| url | https://arxiv.org/abs/2411.15975 |