Effective theory building and manifold learning.
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-dimensiona...
| Publicado en: | Synthese Vol. 205; no. 1; pp. 1 - 34 |
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| Formato: | Artículo |
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Springer Nature
Jan2025
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| Acceso en línea: | Ver este registro en EBSCOhost |
| fields | @attributes: recordID: 1 pdfLink: plink: https://search.ebscohost.com/login.aspx?direct=true&db=hlh&AN=182106123&site=ehost-live header: @attributes: shortDbName: hlh uiTerm: 182106123 longDbName: Humanities International Complete uiTag: AN controlInfo: bkinfo: jinfo: jid: 00397857 4LI jtl: Synthese issn: 00397857 maglogo: N pubinfo: dt: Jan2025 vid: 205 iid: 1 pid: 237 pub: Springer Nature artinfo: ui: 182106123 10.1007/s11229-024-04844-0 ppf: 1 ppct: 33 formats: fmt: – @attributes: type: T – @attributes: type: P size: 3.3MB tig: atl: Effective theory building and manifold learning. aug: au: Freeborn, David Peter Wallis affil: https://ror.org/03hdf3w38 Northeastern University, London Devon House, 58 St Katharine’s Way, E1W 1LP, London, UK sug: keyword: Computational Modeling Effective Theories Machine Learning Manifold Learning Renormalization Group Scientific Models Computergestützte Modellierung Effektive Theorien Maschinelles Lernen Renormierungsgruppe Wissenschaftliche Modelle Aprendizaje automático Aprendizaje de variedades Grupo de renormalización Modelado computacional Modelos científicos Teorías efectivas Apprentissage automatique Apprentissage de variétés Groupe de renormalisation Modèles scientifiques Modélisation computationnelle Théories effectives ab: 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. pubtype: Academic Journal doctype: Article src: R language: English refInfo: copyright: @attributes: flag: Y custom: Synthese is a copyright of Springer, 2025. All Rights Reserved. item: Synthese holder: Springer Nature dt: @attributes: year: 2025 holdings: @attributes: islocal: N |
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