The physics of artificial intelligence
Name
1126279094-MIT.pdf
Size
17.34 MB
Format
Adobe PDF
Checksum (MD5)
221a45f0705c484067ca16fcc6e242f9
Author(s)
Peurifoy, John Edward.
Advisor(s)
Max E. Tegmark.
Date Issued
2018
Publisher
Massachusetts Institute of Technology
Abstract
In this thesis, I explore both what Physics can lend to the world of artificial intelligence, and how artificial intelligence can enhance the world of physics. In the first chapter I propose a method to use artificial neural networks to approximate light scattering by multilayer nanoparticles. This neural network model is experimentally shown to describe the system well, and is then further used to solve the inverse design problem and propose a generalized template for how to use neural networks to enhance numerical calculations. In the second and third chapter I explore the use of Unitary matrices in neural networks to attempt to solve the exploding and vanishing gradient problem. The norm-preserving property of unitary matrices is shown through experiments to allow neural networks to retain information over many more layers. This model achieves state of the art results on a number of toy and real world tasks.
Description
Thesis: S.B., Massachusetts Institute of Technology, Department of Physics, 2018
Cataloged from PDF version of thesis.
Includes bibliographical references (pages 83-87).
Subjects
Physics.
MIT Department
Massachusetts Institute of Technology. Department of Physics
Terms of Use
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