서브메뉴
검색
Neural Operator for Scientific Computing
Neural Operator for Scientific Computing
상세정보
- 자료유형
- 학위논문 서양
- 최종처리일시
- 20260202105058
- ISBN
- 9798288817830
- DDC
- 515.35
- 저자명
- Li, Zongyi.
- 서명/저자
- Neural Operator for Scientific Computing
- 발행사항
- [Sl] : California Institute of Technology, 2025
- 발행사항
- Ann Arbor : ProQuest Dissertations & Theses, 2025
- 형태사항
- 310 p
- 주기사항
- Source: Dissertations Abstracts International, Volume: 87-01, Section: B.
- 주기사항
- Advisor: Anandkumar, Anima.
- 학위논문주기
- Thesis (Ph.D.)--California Institute of Technology, 2025.
- 초록/해제
- 요약Scientific computing, which aims to accurately simulate complex physical phenomena, often requires substantial computational resources. By viewing data as continuous functions, we leverage the smoothness structures of function spaces to enable efficient large-scale simulations. We introduce the neural operator, a universal machine learning framework designed to approximate solution operators in infinite-dimensional spaces, achieving scalable physical simulations. The thesis begins with the introduction and definition of neural operators. Chapters 2-4 discuss architecture designs of neural operators including graph neural operator, multipole neural operator, and Fourier neural operator. Chapters 5-7 discuss physics-based learning techniques such as dissipative loss, physics-informed loss, and scale consistency loss. Chapters 8-10 discuss geometric neural operators with various boundary shapes, including latent space embedding, learned deformation, and optimal transport. Chapters 11-12 discuss further applications of neural operator in weather forecast and carbon capture storage.
- 일반주제명
- Inverse problems
- 일반주제명
- Neural networks
- 일반주제명
- Visualization
- 일반주제명
- Computer science
- 기타저자
- California Institute of Technology Engineering and Applied Science
- 기본자료저록
- Dissertations Abstracts International. 87-01B.
- 전자적 위치 및 접속
- 로그인 후 원문을 볼 수 있습니다.
MARC
008260126s2025 us c eng d■001000017359305
■00520260202105058
■006m o d
■007cr#unu||||||||
■020 ▼a9798288817830
■035 ▼a(MiAaPQ)AAI32205959
■035 ▼a(MiAaPQ)Caltech17396
■040 ▼aMiAaPQ▼cMiAaPQ
■0820 ▼a515.35
■1001 ▼aLi, Zongyi.▼0(orcid)0000-0003-2081-9665
■24510▼aNeural Operator for Scientific Computing
■260 ▼a[Sl]▼bCalifornia Institute of Technology▼c2025
■260 1▼aAnn Arbor▼bProQuest Dissertations & Theses▼c2025
■300 ▼a310 p
■500 ▼aSource: Dissertations Abstracts International, Volume: 87-01, Section: B.
■500 ▼aAdvisor: Anandkumar, Anima.
■5021 ▼aThesis (Ph.D.)--California Institute of Technology, 2025.
■520 ▼aScientific computing, which aims to accurately simulate complex physical phenomena, often requires substantial computational resources. By viewing data as continuous functions, we leverage the smoothness structures of function spaces to enable efficient large-scale simulations. We introduce the neural operator, a universal machine learning framework designed to approximate solution operators in infinite-dimensional spaces, achieving scalable physical simulations. The thesis begins with the introduction and definition of neural operators. Chapters 2-4 discuss architecture designs of neural operators including graph neural operator, multipole neural operator, and Fourier neural operator. Chapters 5-7 discuss physics-based learning techniques such as dissipative loss, physics-informed loss, and scale consistency loss. Chapters 8-10 discuss geometric neural operators with various boundary shapes, including latent space embedding, learned deformation, and optimal transport. Chapters 11-12 discuss further applications of neural operator in weather forecast and carbon capture storage.
■590 ▼aSchool code: 0037.
■650 4▼aPartial differential equations
■650 4▼aInverse problems
■650 4▼aNeural networks
■650 4▼aVisualization
■650 4▼aComputer science
■690 ▼a0984
■690 ▼a0800
■71020▼aCalifornia Institute of Technology▼bEngineering and Applied Science.
■7730 ▼tDissertations Abstracts International▼g87-01B.
■790 ▼a0037
■791 ▼aPh.D.
■792 ▼a2025
■793 ▼aEnglish
■85640▼uhttp://www.riss.kr/pdu/ddodLink.do?id=T17359305▼nKERIS▼z이 자료의 원문은 한국교육학술정보원에서 제공합니다.
