Machine Learning for Nonlinear Materials Characterization and Modeling
Machine Learning for Nonlinear Materials Characterization and Modeling
Detailed Information
- 자료유형
- 학위논문파일 국외
- ISBN
- 9798535507170
- DDC
- 530
- 저자명
- Shea, Daniel e.
- 서명/저자
- Machine Learning for Nonlinear Materials Characterization and Modeling
- 발행사항
- [Sl] : University of Washington, 2021
- 발행사항
- Ann Arbor : ProQuest Dissertations & Theses, 2021
- 형태사항
- 130 p
- 주기사항
- Source: Dissertations Abstracts International, Volume: 83-02, Section: B.
- 주기사항
- Advisor: Kutz, J. Nathan;Brunton, Steven L.
- 학위논문주기
- Thesis (Ph.D.)--University of Washington, 2021.
- 사용제한주기
- This item must not be sold to any third party vendors.
- 일반주제명
- Computational physics
- 일반주제명
- Applied mathematics
- 일반주제명
- Materials science
- 일반주제명
- Deep learning
- 일반주제명
- Datasets
- 일반주제명
- Collaboration
- 일반주제명
- Identification
- 일반주제명
- Dissertations & theses
- 일반주제명
- Eigen values
- 일반주제명
- Decomposition
- 일반주제명
- Noise
- 일반주제명
- Physical sciences
- 일반주제명
- Time series
- 일반주제명
- Dynamical systems
- 일반주제명
- 20th century
- 일반주제명
- Boundary value problems
- 일반주제명
- Heat transfer
- 일반주제명
- Quantum physics
- 일반주제명
- Oscillators
- 일반주제명
- Engineering
- 일반주제명
- Algorithms
- 키워드
- Deep learning
- 키워드
- Machine learning
- 기타저자
- University of Washington Materials Science and Engineering
- 기본자료저록
- Dissertations Abstracts International. 83-02B.
- 기본자료저록
- Dissertation Abstract International
- 전자적 위치 및 접속
- 로그인 후 원문을 볼 수 있습니다.
MARC
008220131s2021 us c eng d■020 ▼a9798535507170
■035 ▼a(MiAaPQ)AAI28546948
■040 ▼aMiAaPQ▼cMiAaPQ
■0820 ▼a530
■1001 ▼aShea, Daniel e.
■24510▼aMachine Learning for Nonlinear Materials Characterization and Modeling
■260 ▼a[Sl]▼bUniversity of Washington▼c2021
■260 1▼aAnn Arbor▼bProQuest Dissertations & Theses▼c2021
■300 ▼a130 p
■500 ▼aSource: Dissertations Abstracts International, Volume: 83-02, Section: B.
■500 ▼aAdvisor: Kutz, J. Nathan;Brunton, Steven L.
■5021 ▼aThesis (Ph.D.)--University of Washington, 2021.
■506 ▼aThis item must not be sold to any third party vendors.
■590 ▼aSchool code: 0250.
■650 4▼aComputational physics
■650 4▼aApplied mathematics
■650 4▼aMaterials science
■650 4▼aDeep learning
■650 4▼aDatasets
■650 4▼aCollaboration
■650 4▼aIdentification
■650 4▼aDissertations & theses
■650 4▼aEigen values
■650 4▼aDecomposition
■650 4▼aNoise
■650 4▼aPhysical sciences
■650 4▼aTime series
■650 4▼aDynamical systems
■650 4▼a20th century
■650 4▼aBoundary value problems
■650 4▼aHeat transfer
■650 4▼aQuantum physics
■650 4▼aPartial differential equations
■650 4▼aCoordinate transformations
■650 4▼aOscillators
■650 4▼aEngineering
■650 4▼aAlgorithms
■653 ▼aBoundary value problems
■653 ▼aDeep learning
■653 ▼aMachine learning
■653 ▼aMaterials science and engineering
■653 ▼aSignal decomposition
■690 ▼a0216
■690 ▼a0794
■690 ▼a0364
■690 ▼a0599
■690 ▼a0537
■71020▼aUniversity of Washington▼bMaterials Science and Engineering.
■7730 ▼tDissertations Abstracts International▼g83-02B.
■773 ▼tDissertation Abstract International
■790 ▼a0250
■791 ▼aPh.D.
■792 ▼a2021
■793 ▼aEnglish
■85640▼uhttp://www.riss.kr/pdu/ddodLink.do?id=T16052778▼nKERIS▼z이 자료의 원문은 한국교육학술정보원에서 제공합니다.
■980 ▼a202202▼f2022
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