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The Geometry and Statistical Physics of Complex Systems
The Geometry and Statistical Physics of Complex Systems
상세정보
- 자료유형
- 학위논문 서양
- 최종처리일시
- 20260202103625
- ISBN
- 9798286498963
- DDC
- 574.191
- 저자명
- Praturu, Anoop.
- 서명/저자
- The Geometry and Statistical Physics of Complex Systems
- 발행사항
- [Sl] : University of California, San Diego, 2025
- 발행사항
- Ann Arbor : ProQuest Dissertations & Theses, 2025
- 형태사항
- 87 p
- 주기사항
- Source: Dissertations Abstracts International, Volume: 87-01, Section: B.
- 주기사항
- Advisor: Sharpee, Tatyana;Kleinfeld, David.
- 학위논문주기
- Thesis (Ph.D.Physics.)--University of California, San Diego, 2025.
- 초록/해제
- 요약Biological, and complex systems more broadly, are characterized by their many interacting degrees of freedom. Despite their apparent complexity and scale, the microscopic constituents of these systems often act in concert to produce emergent macroscopic behavior with vastly reduced dimension. Through the lens of geometry and statistical physics, we present a collection of novel approaches to dimensionality reduction in complex systems identify their correct low dimensional description. We take a threefold approach based on embedding methods, matrix factorization, and neural compression.
- 일반주제명
- Biophysics
- 일반주제명
- Statistical physics
- 일반주제명
- Physics
- 키워드
- Geometry
- 키워드
- Complex systems
- 기타저자
- University of California, San Diego Physics
- 기본자료저록
- Dissertations Abstracts International. 87-01B.
- 전자적 위치 및 접속
- 로그인 후 원문을 볼 수 있습니다.
MARC
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■020 ▼a9798286498963
■035 ▼a(MiAaPQ)AAI32046324
■040 ▼aMiAaPQ▼cMiAaPQ
■0820 ▼a574.191
■1001 ▼aPraturu, Anoop.
■24510▼aThe Geometry and Statistical Physics of Complex Systems
■260 ▼a[Sl]▼bUniversity of California, San Diego▼c2025
■260 1▼aAnn Arbor▼bProQuest Dissertations & Theses▼c2025
■300 ▼a87 p
■500 ▼aSource: Dissertations Abstracts International, Volume: 87-01, Section: B.
■500 ▼aAdvisor: Sharpee, Tatyana;Kleinfeld, David.
■5021 ▼aThesis (Ph.D.Physics.)--University of California, San Diego, 2025.
■520 ▼aBiological, and complex systems more broadly, are characterized by their many interacting degrees of freedom. Despite their apparent complexity and scale, the microscopic constituents of these systems often act in concert to produce emergent macroscopic behavior with vastly reduced dimension. Through the lens of geometry and statistical physics, we present a collection of novel approaches to dimensionality reduction in complex systems identify their correct low dimensional description. We take a threefold approach based on embedding methods, matrix factorization, and neural compression.
■590 ▼aSchool code: 0033.
■650 4▼aBiophysics
■650 4▼aStatistical physics
■650 4▼aPhysics
■653 ▼aGeometry
■653 ▼aComplex systems
■653 ▼aNeural compression
■653 ▼aMatrix factorization
■653 ▼aEmbedding methods
■690 ▼a0786
■690 ▼a0217
■690 ▼a0605
■71020▼aUniversity of California, San Diego▼bPhysics.
■7730 ▼tDissertations Abstracts International▼g87-01B.
■790 ▼a0033
■791 ▼aPh.D.Physics.
■792 ▼a2025
■793 ▼aEnglish
■85640▼uhttp://www.riss.kr/pdu/ddodLink.do?id=T17357974▼nKERIS▼z이 자료의 원문은 한국교육학술정보원에서 제공합니다.
