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Algorithms for the Nuclear Many-Body Problem and Beyond
Algorithms for the Nuclear Many-Body Problem and Beyond
상세정보
- 자료유형
- 학위논문 서양
- 최종처리일시
- 20250211152936
- ISBN
- 9798896076087
- DDC
- 530
- 저자명
- Hicks, Ashe.
- 서명/저자
- Algorithms for the Nuclear Many-Body Problem and Beyond
- 발행사항
- [Sl] : Michigan State University, 2024
- 발행사항
- Ann Arbor : ProQuest Dissertations & Theses, 2024
- 형태사항
- 91 p
- 주기사항
- Source: Dissertations Abstracts International, Volume: 86-04, Section: B.
- 주기사항
- Advisor: Lee, Dean.
- 학위논문주기
- Thesis (Ph.D.)--Michigan State University, 2024.
- 초록/해제
- 요약The nuclear many body problem allows us to take our fundamental understanding of the most basic building blocks of the universe and from them build an understanding of larger and more complicated systems. It is the essential problem of how individual particles form atoms and larger structures. Its applications are varied, and many tools have been developed to address this problem. Despite the breakneck pace of computational development, the nuclear many-body problem still stretches our computational and numerical methods to and beyond their breaking points. In this work, we introduce two algorithms which can help in solving the nuclear many-body problem. First, we introduce trimmed sampling. This is an algorithm which can be used to treat noisy data obtained from highly sensitive calculations, particularly the generalized eigenvalue problem which emerges from a number of techniques. We solve a number of example models for which small errors such as rounding error or statistical noise are sufficient to entirely destroy any usable results, but see that trimmed sampling is able to recover good results from these methods. It does so using Bayesian inference, by applying physics-informed criteria and statistical sampling methods we are able to eliminate any solutions which are non-physical, leaving a more accurate, physically meaningful result. We show ways that this algorithm can be further expanded and enhanced, improving sampling statistics, convergence rate, and accuracy, before demonstrating its performance on the Lipkin model. In the next section, we describe the Projected Cooling algorithm. This is a method whereby we use an analogue of evaporative cooling to calculate the ground state of a system. We show results of projected cooling for several models. Together, this work provides a description of useful algorithms which can be applied to the nuclear many-body problem.
- 일반주제명
- Computational physics
- 일반주제명
- Nuclear physics
- 일반주제명
- Quantum physics
- 키워드
- Lipkin
- 키워드
- Trimmed sampling
- 기타저자
- Michigan State University Physics - Doctor of Philosophy
- 기본자료저록
- Dissertations Abstracts International. 86-04B.
- 전자적 위치 및 접속
- 로그인 후 원문을 볼 수 있습니다.
MARC
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■006m o d
■007cr#unu||||||||
■020 ▼a9798896076087
■035 ▼a(MiAaPQ)AAI31563565
■040 ▼aMiAaPQ▼cMiAaPQ
■0820 ▼a530
■1001 ▼aHicks, Ashe.
■24510▼aAlgorithms for the Nuclear Many-Body Problem and Beyond
■260 ▼a[Sl]▼bMichigan State University▼c2024
■260 1▼aAnn Arbor▼bProQuest Dissertations & Theses▼c2024
■300 ▼a91 p
■500 ▼aSource: Dissertations Abstracts International, Volume: 86-04, Section: B.
■500 ▼aAdvisor: Lee, Dean.
■5021 ▼aThesis (Ph.D.)--Michigan State University, 2024.
■520 ▼aThe nuclear many body problem allows us to take our fundamental understanding of the most basic building blocks of the universe and from them build an understanding of larger and more complicated systems. It is the essential problem of how individual particles form atoms and larger structures. Its applications are varied, and many tools have been developed to address this problem. Despite the breakneck pace of computational development, the nuclear many-body problem still stretches our computational and numerical methods to and beyond their breaking points. In this work, we introduce two algorithms which can help in solving the nuclear many-body problem. First, we introduce trimmed sampling. This is an algorithm which can be used to treat noisy data obtained from highly sensitive calculations, particularly the generalized eigenvalue problem which emerges from a number of techniques. We solve a number of example models for which small errors such as rounding error or statistical noise are sufficient to entirely destroy any usable results, but see that trimmed sampling is able to recover good results from these methods. It does so using Bayesian inference, by applying physics-informed criteria and statistical sampling methods we are able to eliminate any solutions which are non-physical, leaving a more accurate, physically meaningful result. We show ways that this algorithm can be further expanded and enhanced, improving sampling statistics, convergence rate, and accuracy, before demonstrating its performance on the Lipkin model. In the next section, we describe the Projected Cooling algorithm. This is a method whereby we use an analogue of evaporative cooling to calculate the ground state of a system. We show results of projected cooling for several models. Together, this work provides a description of useful algorithms which can be applied to the nuclear many-body problem.
■590 ▼aSchool code: 0128.
■650 4▼aComputational physics
■650 4▼aNuclear physics
■650 4▼aQuantum physics
■653 ▼aLipkin
■653 ▼aMarkov Chain Monte Carlo
■653 ▼aNuclear many-body problem
■653 ▼aTrimmed sampling
■690 ▼a0756
■690 ▼a0216
■690 ▼a0599
■71020▼aMichigan State University▼bPhysics - Doctor of Philosophy.
■7730 ▼tDissertations Abstracts International▼g86-04B.
■790 ▼a0128
■791 ▼aPh.D.
■792 ▼a2024
■793 ▼aEnglish
■85640▼uhttp://www.riss.kr/pdu/ddodLink.do?id=T17164229▼nKERIS▼z이 자료의 원문은 한국교육학술정보원에서 제공합니다.
