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Toward Efficient and Robust Physical Simulation and Physics-Guided Content Generation
Toward Efficient and Robust Physical Simulation and Physics-Guided Content Generation
상세정보
- 자료유형
- 학위논문 서양
- 최종처리일시
- 20260202103613
- ISBN
- 9798315748694
- DDC
- 519
- 저자명
- Zong, Zeshun.
- 서명/저자
- Toward Efficient and Robust Physical Simulation and Physics-Guided Content Generation
- 발행사항
- [Sl] : University of California, Los Angeles, 2025
- 발행사항
- Ann Arbor : ProQuest Dissertations & Theses, 2025
- 형태사항
- 176 p
- 주기사항
- Source: Dissertations Abstracts International, Volume: 86-11, Section: B.
- 주기사항
- Advisor: Jiang, Chenfanfu.
- 학위논문주기
- Thesis (Ph.D.)--University of California, Los Angeles, 2025.
- 초록/해제
- 요약Physical simulation has long been a cornerstone of computer graphics, driving the generation of vivid dynamics across a variety of forms, from movies to video games. In recent years, the development of machine learning algorithms, along with the rise of massively parallel hardware such as GPUs, has created new opportunities as well as new challenges for traditional physical simulation. For instance, mobile devices such as smartphones and AR/VR glasses demand high-speed simulations on resource-constrained hardware. Similarly, the fields of embodied AI and robotics require fast and robust physical solvers capable of handling complex interactions. Moreover, advances in modern vision techniques and generative models have opened new avenues for using physical simulation to create novel digital content.In this dissertation, we first present novel reduced-order modeling techniques to accelerate existing physical simulation methods. Next, we introduce a robust rigid-deformable simulation method tailored for robotic applications. Finally, we explore how bridging physical simulation with state-of-the-art vision techniques enables dynamic novel view synthesis, and how combining physical simulation with 3D generative models facilitates the creation of 3D assets that stably interact with gravity, contact, and friction.
- 일반주제명
- Applied mathematics
- 일반주제명
- Computational physics
- 일반주제명
- Robotics
- 키워드
- Computer vision
- 키워드
- Machine learning
- 기타저자
- University of California, Los Angeles Mathematics 0540
- 기본자료저록
- Dissertations Abstracts International. 86-11B.
- 전자적 위치 및 접속
- 로그인 후 원문을 볼 수 있습니다.
MARC
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■020 ▼a9798315748694
■035 ▼a(MiAaPQ)AAI32043691
■040 ▼aMiAaPQ▼cMiAaPQ
■0820 ▼a519
■1001 ▼aZong, Zeshun.
■24510▼aToward Efficient and Robust Physical Simulation and Physics-Guided Content Generation
■260 ▼a[Sl]▼bUniversity of California, Los Angeles▼c2025
■260 1▼aAnn Arbor▼bProQuest Dissertations & Theses▼c2025
■300 ▼a176 p
■500 ▼aSource: Dissertations Abstracts International, Volume: 86-11, Section: B.
■500 ▼aAdvisor: Jiang, Chenfanfu.
■5021 ▼aThesis (Ph.D.)--University of California, Los Angeles, 2025.
■520 ▼aPhysical simulation has long been a cornerstone of computer graphics, driving the generation of vivid dynamics across a variety of forms, from movies to video games. In recent years, the development of machine learning algorithms, along with the rise of massively parallel hardware such as GPUs, has created new opportunities as well as new challenges for traditional physical simulation. For instance, mobile devices such as smartphones and AR/VR glasses demand high-speed simulations on resource-constrained hardware. Similarly, the fields of embodied AI and robotics require fast and robust physical solvers capable of handling complex interactions. Moreover, advances in modern vision techniques and generative models have opened new avenues for using physical simulation to create novel digital content.In this dissertation, we first present novel reduced-order modeling techniques to accelerate existing physical simulation methods. Next, we introduce a robust rigid-deformable simulation method tailored for robotic applications. Finally, we explore how bridging physical simulation with state-of-the-art vision techniques enables dynamic novel view synthesis, and how combining physical simulation with 3D generative models facilitates the creation of 3D assets that stably interact with gravity, contact, and friction.
■590 ▼aSchool code: 0031.
■650 4▼aApplied mathematics
■650 4▼aComputational physics
■650 4▼aRobotics
■653 ▼aComputer graphics
■653 ▼aComputer vision
■653 ▼aMachine learning
■653 ▼aPhysical simulation
■690 ▼a0364
■690 ▼a0800
■690 ▼a0216
■690 ▼a0771
■71020▼aUniversity of California, Los Angeles▼bMathematics 0540.
■7730 ▼tDissertations Abstracts International▼g86-11B.
■790 ▼a0031
■791 ▼aPh.D.
■792 ▼a2025
■793 ▼aEnglish
■85640▼uhttp://www.riss.kr/pdu/ddodLink.do?id=T17357880▼nKERIS▼z이 자료의 원문은 한국교육학술정보원에서 제공합니다.
