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Physics-Guided Deep Learning for Dynamics Forecasting- [electronic resource]
![Physics-Guided Deep Learning for Dynamics Forecasting - [electronic resource]](/Users/Baul/Images/book.png)
Physics-Guided Deep Learning for Dynamics Forecasting- [electronic resource]
상세정보
- 자료유형
- 학위논문파일 국외
- 최종처리일시
- 20240214100112
- ISBN
- 9798379762131
- DDC
- 004
- 저자명
- Wang, Rui.
- 서명/저자
- Physics-Guided Deep Learning for Dynamics Forecasting - [electronic resource]
- 발행사항
- [S.l.]: : University of California, San Diego., 2023
- 발행사항
- Ann Arbor : : ProQuest Dissertations & Theses,, 2023
- 형태사항
- 1 online resource(138 p.)
- 주기사항
- Source: Dissertations Abstracts International, Volume: 85-01, Section: B.
- 주기사항
- Advisor: Yu, Rose.
- 학위논문주기
- Thesis (Ph.D.)--University of California, San Diego, 2023.
- 사용제한주기
- This item must not be sold to any third party vendors.
- 초록/해제
- 요약Modeling complex dynamics is a fundamental task in science, such as turbulence modeling and weather forecasting. Physics-based models, which rely on mathematical principles, can accurately predict dynamics but can be computationally intensive and not fully known. Deep Learning provides efficient alternatives to simulating dynamics but it lacks physical consistency and struggles with generalization. Thus, there is a growing need for integrating prior physics knowledge with deep learning to take the best of both types of approaches to better solve scientific problems. Thus, the study of physics-guided DL emerged and has gained great progress. In this thesis, we described the physics-guide DL for dynamics forecasting and presented several approaches to improving the physical consistency, accuracy, and generalization of DL models for dynamics forecasting. The approaches include incorporating prior physical knowledge into the design of model architecture and loss functions for improved physical consistency and accuracy, leveraging model-based meta-learning for improved generalization across heterogeneous domains, simplifying nonlinear dynamics with Koopman theory for improved generalization over temporal distributional shifts, and incorporating symmetries into deep dynamics models for improved generalization across relevant symmetry groups and consistency with conservation laws. In the end, we also summarize the challenges in this field and discuss the emerging opportunities for future research.
- 일반주제명
- Computer science.
- 일반주제명
- Computer engineering.
- 키워드
- AI for Science
- 키워드
- Deep Learning
- 키워드
- Machine learning
- 키워드
- Symmetry
- 기타저자
- University of California, San Diego Computer Science and Engineering
- 기본자료저록
- Dissertations Abstracts International. 85-01B.
- 기본자료저록
- Dissertation Abstract International
- 전자적 위치 및 접속
- 로그인 후 원문을 볼 수 있습니다.
MARC
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■00520240214100112
■006m o d
■007cr#unu||||||||
■020 ▼a9798379762131
■035 ▼a(MiAaPQ)AAI30421717
■040 ▼aMiAaPQ▼cMiAaPQ
■0820 ▼a004
■1001 ▼aWang, Rui.
■24510▼aPhysics-Guided Deep Learning for Dynamics Forecasting▼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(138 p.)
■500 ▼aSource: Dissertations Abstracts International, Volume: 85-01, Section: B.
■500 ▼aAdvisor: Yu, Rose.
■5021 ▼aThesis (Ph.D.)--University of California, San Diego, 2023.
■506 ▼aThis item must not be sold to any third party vendors.
■520 ▼aModeling complex dynamics is a fundamental task in science, such as turbulence modeling and weather forecasting. Physics-based models, which rely on mathematical principles, can accurately predict dynamics but can be computationally intensive and not fully known. Deep Learning provides efficient alternatives to simulating dynamics but it lacks physical consistency and struggles with generalization. Thus, there is a growing need for integrating prior physics knowledge with deep learning to take the best of both types of approaches to better solve scientific problems. Thus, the study of physics-guided DL emerged and has gained great progress. In this thesis, we described the physics-guide DL for dynamics forecasting and presented several approaches to improving the physical consistency, accuracy, and generalization of DL models for dynamics forecasting. The approaches include incorporating prior physical knowledge into the design of model architecture and loss functions for improved physical consistency and accuracy, leveraging model-based meta-learning for improved generalization across heterogeneous domains, simplifying nonlinear dynamics with Koopman theory for improved generalization over temporal distributional shifts, and incorporating symmetries into deep dynamics models for improved generalization across relevant symmetry groups and consistency with conservation laws. In the end, we also summarize the challenges in this field and discuss the emerging opportunities for future research.
■590 ▼aSchool code: 0033.
■650 4▼aComputer science.
■650 4▼aComputer engineering.
■653 ▼aAI for Science
■653 ▼aDeep Learning
■653 ▼aDynamical systems
■653 ▼aMachine learning
■653 ▼aSpatiotemporal modeling
■653 ▼aSymmetry
■690 ▼a0800
■690 ▼a0984
■690 ▼a0464
■71020▼aUniversity of California, San Diego▼bComputer Science and Engineering.
■7730 ▼tDissertations Abstracts International▼g85-01B.
■773 ▼tDissertation Abstract International
■790 ▼a0033
■791 ▼aPh.D.
■792 ▼a2023
■793 ▼aEnglish
■85640▼uhttp://www.riss.kr/pdu/ddodLink.do?id=T16931745▼nKERIS▼z이 자료의 원문은 한국교육학술정보원에서 제공합니다.
■980 ▼a202402▼f2024
