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Dealings with Data: Physics, Machine...

Hayden, Lorien Xanthe.

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Dealings with Data: Physics, Machine Learning and Geometry.

Record Type:	Electronic resources : Monograph/item
Title/Author:	Dealings with Data: Physics, Machine Learning and Geometry./
Author:	Hayden, Lorien Xanthe.
Published:	Ann Arbor : ProQuest Dissertations & Theses, : 2019,
Description:	149 p.
Notes:	Source: Dissertations Abstracts International, Volume: 81-04, Section: B.
Contained By:	Dissertations Abstracts International81-04B.
Subject:	Computational physics. -
Online resource:	http://pqdd.sinica.edu.tw/twdaoapp/servlet/advanced?query=22616491
ISBN:	9781088392591

Dealings with Data: Physics, Machine Learning and Geometry.
Hayden, Lorien Xanthe.

Dealings with Data: Physics, Machine Learning and Geometry. - Ann Arbor : ProQuest Dissertations & Theses, 2019 - 149 p.

Source: Dissertations Abstracts International, Volume: 81-04, Section: B.

Thesis (Ph.D.)--Cornell University, 2019.

This item must not be sold to any third party vendors.

Collecting and interpreting data is key to developing an understanding of the physical underpinnings of observable events. As such, questions of how to generate, curate and otherwise wrangle data become central as systems of interest become increasingly difficult to access experimentally and the sheer quantity of raw information explodes.The data explored in this dissertation covers a wide range of sources and methods. On the more traditional end, we explore simulation data of the two dimensional non-equilibrium random-field Ising model which we treat with a novel analytic normal form theory of the Renormalization Group. Branching out from condensed matter, we explore several machine learning and sampling methods in various contexts.The machine learning projects in particular include three lines of investigation: an unsupervised machine learning analysis of sectors of the economy extracted from stock return data, an analysis of the computational neural networks successfully applied to experimental ATLAS data in a recent Kaggle challenge, and an exploration of the geometrical underpinnings of canonical neural networks using a Jeffrey's Prior sampling of trained networks.

ISBN: 9781088392591Subjects--Topical Terms:

3343998
Computational physics.
Subjects--Index Terms:

Data

Dealings with Data: Physics, Machine Learning and Geometry.
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245 1 0 $a Dealings with Data: Physics, Machine Learning and Geometry.
260 1 $a Ann Arbor : $b ProQuest Dissertations & Theses, $c 2019
300 $a 149 p.
500 $a Source: Dissertations Abstracts International, Volume: 81-04, Section: B.
500 $a Advisor: Sethna, James Patarasp.
502 $a Thesis (Ph.D.)--Cornell University, 2019.
506 $a This item must not be sold to any third party vendors.
520 $a Collecting and interpreting data is key to developing an understanding of the physical underpinnings of observable events. As such, questions of how to generate, curate and otherwise wrangle data become central as systems of interest become increasingly difficult to access experimentally and the sheer quantity of raw information explodes.The data explored in this dissertation covers a wide range of sources and methods. On the more traditional end, we explore simulation data of the two dimensional non-equilibrium random-field Ising model which we treat with a novel analytic normal form theory of the Renormalization Group. Branching out from condensed matter, we explore several machine learning and sampling methods in various contexts.The machine learning projects in particular include three lines of investigation: an unsupervised machine learning analysis of sectors of the economy extracted from stock return data, an analysis of the computational neural networks successfully applied to experimental ATLAS data in a recent Kaggle challenge, and an exploration of the geometrical underpinnings of canonical neural networks using a Jeffrey's Prior sampling of trained networks.
590 $a School code: 0058.
650 4 $a Computational physics. $3 3343998
650 4 $a Mathematics. $3 515831
650 4 $a Artificial intelligence. $3 516317
653 $a Data
653 $a Physics
653 $a Machine learning
653 $a Geometry
690 $a 0216
690 $a 0800
690 $a 0405
710 2 $a Cornell University. $b Physics. $3 3173885
773 0 $t Dissertations Abstracts International $g 81-04B.
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791 $a Ph.D.
792 $a 2019
793 $a English
856 4 0 $u http://pqdd.sinica.edu.tw/twdaoapp/servlet/advanced?query=22616491