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Generative Neural Networks Enable Real-Time EM Metastructure Designs- [electronic resource]
Generative Neural Networks Enable Real-Time EM Metastructure Designs- [electronic resource]
상세정보
- 자료유형
- 학위논문파일 국외
- 최종처리일시
- 20240214101509
- ISBN
- 9798380472630
- DDC
- 621.3
- 저자명
- Wen, Erda.
- 서명/저자
- Generative Neural Networks Enable Real-Time EM Metastructure Designs - [electronic resource]
- 발행사항
- [S.l.]: : University of California, San Diego., 2023
- 발행사항
- Ann Arbor : : ProQuest Dissertations & Theses,, 2023
- 형태사항
- 1 online resource(80 p.)
- 주기사항
- Source: Dissertations Abstracts International, Volume: 85-04, Section: B.
- 주기사항
- Advisor: Sievenpiper, Daniel F.
- 학위논문주기
- Thesis (Ph.D.)--University of California, San Diego, 2023.
- 사용제한주기
- This item must not be sold to any third party vendors.
- 초록/해제
- 요약This dissertation discusses how generative-type artificial neural network (ANNs) enables the real-time inverse design process of reconfigurable EM megastructures. Specifically, we demonstrate two designs: 1. a 2-D beamformer with rotatable dielectric slabs and 2. a conformal EM coating responding to free-form design goals on reflection pattern in a dynamic environment, the latter being an ultimate ambition of EM scattering control and nearly impossible to realize with conventional methods. These two examples demonstrate the superiority of ANN methods in dealing with high-nonlinear EM design problems requiring fast responses. Furthermore, the proposed data-driven free-form inverse-design approach can be accommodated for other science/engineering tasks.
- 일반주제명
- Electrical engineering.
- 키워드
- Dielectric slabs
- 기타저자
- University of California, San Diego Computer Science and Engineering
- 기본자료저록
- Dissertations Abstracts International. 85-04B.
- 기본자료저록
- Dissertation Abstract International
- 전자적 위치 및 접속
- 로그인 후 원문을 볼 수 있습니다.
MARC
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■00520240214101509
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■020 ▼a9798380472630
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■040 ▼aMiAaPQ▼cMiAaPQ
■0820 ▼a621.3
■1001 ▼aWen, Erda.
■24510▼aGenerative Neural Networks Enable Real-Time EM Metastructure Designs▼h[electronic resource]
■260 ▼a[S.l.]:▼bUniversity of California, San Diego. ▼c2023
■260 1▼aAnn Arbor :▼bProQuest Dissertations & Theses, ▼c2023
■300 ▼a1 online resource(80 p.)
■500 ▼aSource: Dissertations Abstracts International, Volume: 85-04, Section: B.
■500 ▼aAdvisor: Sievenpiper, Daniel F.
■5021 ▼aThesis (Ph.D.)--University of California, San Diego, 2023.
■506 ▼aThis item must not be sold to any third party vendors.
■520 ▼aThis dissertation discusses how generative-type artificial neural network (ANNs) enables the real-time inverse design process of reconfigurable EM megastructures. Specifically, we demonstrate two designs: 1. a 2-D beamformer with rotatable dielectric slabs and 2. a conformal EM coating responding to free-form design goals on reflection pattern in a dynamic environment, the latter being an ultimate ambition of EM scattering control and nearly impossible to realize with conventional methods. These two examples demonstrate the superiority of ANN methods in dealing with high-nonlinear EM design problems requiring fast responses. Furthermore, the proposed data-driven free-form inverse-design approach can be accommodated for other science/engineering tasks.
■590 ▼aSchool code: 0033.
■650 4▼aElectrical engineering.
■653 ▼aArtificial neural network
■653 ▼aDynamic environment
■653 ▼aConventional methods
■653 ▼aDielectric slabs
■690 ▼a0544
■690 ▼a0800
■71020▼aUniversity of California, San Diego▼bComputer Science and Engineering.
■7730 ▼tDissertations Abstracts International▼g85-04B.
■773 ▼tDissertation Abstract International
■790 ▼a0033
■791 ▼aPh.D.
■792 ▼a2023
■793 ▼aEnglish
■85640▼uhttp://www.riss.kr/pdu/ddodLink.do?id=T16933939▼nKERIS▼z이 자료의 원문은 한국교육학술정보원에서 제공합니다.
■980 ▼a202402▼f2024
