Classical and Quantum Physics-Enhanced Machine Learning Algorithms in the Ordered and Chaotic Regimes- [electronic resource]
Classical and Quantum Physics-Enhanced Machine Learning Algorithms in the Ordered and Chaotic Regimes- [electronic resource]
- Material Type
- 단행본
- 0016932880
- Date and Time of Latest Transaction
- 20240214100557
- ISBN
- 9798379871154
- DDC
- 517
- Author
- Holliday, Elliott Gregory.
- Title/Author
- Classical and Quantum Physics-Enhanced Machine Learning Algorithms in the Ordered and Chaotic Regimes - [electronic resource]
- Publish Info
- [S.l.]: : North Carolina State University., 2023
- Publish Info
- Ann Arbor : : ProQuest Dissertations & Theses,, 2023
- Material Info
- 1 online resource(107 p.)
- General Note
- Source: Dissertations Abstracts International, Volume: 85-01, Section: B.
- General Note
- Advisor: Kumah, Divine;LeBlanc, Sharonda;Ruffino, Rico;Lindner, John F.;Daniels, Karen;Ditto, William L.
- 학위논문주기
- Thesis (Ph.D.)--North Carolina State University, 2023.
- Restrictions on Access Note
- This item must not be sold to any third party vendors.
- Abstracts/Etc
- 요약Artificial neural networks (ANN) and machine learning have become critical for advancements in science, technology, and daily life. To expand the resources available to physicists for making discoveries and contributions to the field of physics, can we solve classical and quantum physics problems that exhibit both order and chaos using a neural network? Can we improve a neural network's ability to solve physics problems by giving it an internal physics intuition? Noting that calculus lies at the heart of both machine learning algorithms and physics, this thesis incorporates physics into the training process of ANNs to forecast classical Hamiltonian dynamical systems that exhibit both order and chaos such as the Henon-Heiles stellar potential, chaotic billiards, and the double pendulum. While the ANN is only given a singular formalism or set of constraints, what additional knowledge do we discover upon giving a physics formalism to the neural network? I find doing so recovers more about the system than what was inputted such as the energy, the dimensionality, and the fraction of chaotic orbits for a given energy range. While the Hamiltonian requires canonical coordinates, it also expands on the previous algorithm to forecast dynamics without canonical coordinates for the Lotka-Volterra predator-prey model and a video of a wooden pendulum clock. This thesis also develops this idea into quantum mechanics and explores the result of giving an ANN the Schrodinger equation so that it may recover eigenfunctions and energies. This method is tested on previously studied one- and twodimensional systems like the infinite square well and simple harmonic oscillator and two-dimensional infinite potential wells that classically exhibit order and chaos, such as elliptical, triangular, and cardioid-shaped wells. Physics-enhanced machine learning algorithms have the potential to improve how advances in physics and science are made but also could improve current ANNs by giving them scientific principles and knowledge.
- Subject Added Entry-Topical Term
- Calculus.
- Subject Added Entry-Topical Term
- Neurons.
- Subject Added Entry-Topical Term
- Physics.
- Subject Added Entry-Topical Term
- Partial differential equations.
- Subject Added Entry-Topical Term
- Neural networks.
- Subject Added Entry-Topical Term
- Eigenvalues.
- Subject Added Entry-Topical Term
- Energy.
- Subject Added Entry-Topical Term
- Billiards.
- Subject Added Entry-Topical Term
- Mathematics.
- Subject Added Entry-Topical Term
- Quantum physics.
- Added Entry-Corporate Name
- North Carolina State University.
- Host Item Entry
- Dissertations Abstracts International. 85-01B.
- Host Item Entry
- Dissertation Abstract International
- Electronic Location and Access
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- 소장사항
-
202402 2024
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