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Advancing Physics-Based Simulations: Integrating Conventional and Machine-Learning Approaches for Enhanced Computational Efficiency
Advancing Physics-Based Simulations: Integrating Conventional and Machine-Learning Approaches for Enhanced Computational Efficiency
상세정보
- 자료유형
- 학위논문 서양
- 최종처리일시
- 20250211152003
- ISBN
- 9798382829708
- DDC
- 004
- 저자명
- Cao, Yadi.
- 서명/저자
- Advancing Physics-Based Simulations: Integrating Conventional and Machine-Learning Approaches for Enhanced Computational Efficiency
- 발행사항
- [Sl] : University of California, Los Angeles, 2024
- 발행사항
- Ann Arbor : ProQuest Dissertations & Theses, 2024
- 형태사항
- 129 p
- 주기사항
- Source: Dissertations Abstracts International, Volume: 85-12, Section: B.
- 주기사항
- Advisor: Terzopoulos, Demetri;Jiang, Chenfanfu;Terzopoulos, Demetri.
- 학위논문주기
- Thesis (Ph.D.)--University of California, Los Angeles, 2024.
- 초록/해제
- 요약This thesis presents novel approaches to improve the accuracy and efficiency of scientific simulations, particularly those involving complex geometries, intrinsic physical modeling, and demanding computational costs.The first contribution extends the MPM to unstructured meshes, addressing the challenges of the transfer kernel's gradient continuity and stability issue on any general mesh tesselation. The Unstructured Moving Least Squares MPM (UMLS-MPM) incorporates a diminishing function into the MLS kernel's sample weights, ensuring an analytically continuous function and gradient reconstruction. It is the first-of-its-kind framework in this field. Several numerical test cases demonstrate the method's stability and accuracy.The second contribution is a hybrid scheme for modeling the interaction between compressible flow, shock waves, and deformable structures. By combining recent advancements in time-splitting compressible flow and Material Point Methods (MPMs), this approach seamlessly integrates Eulerian and Lagrangian/Eulerian methods for monolithic flow-structure interactions. Reflective and penetrable boundary conditions handle deforming boundaries with sub-cell particles, while a mixed-order finite element formulation utilizing B-spline shape functions discretizes the coupled velocity-pressure system. This comprehensive framework accurately captures shock wave propagation, temperature/density-induced buoyancy effects, and topology changes in solids.The third contribution addresses challenges in learning physical simulations on largescale meshes using Graph Neural Networks (GNNs). Existing state-of-the-art methods often encounter issues related to over-smoothing and incorrect edge construction during multi-scale adaptation. To overcome these limitations, a novel pooling strategy, termed bi-stride, is introduced. This approach, inspired by bipartite graph structures, involves pooling nodes on alternate frontiers of the breadth-first search (BFS), eliminating the need for labor-intensive manual creation of coarser meshes and mitigating incorrect edge problems. The proposed BSMS-GNN framework employs non-parametrized pooling and unpooling through interpolations, resulting in a substantial reduction of computational costs and improved efficiency. Experimental results demonstrate the superiority of the BSMS-GNN framework in terms of both accuracy and computational efficiency in representative physical simulations on large-scale meshes.
- 일반주제명
- Computer science
- 일반주제명
- Engineering
- 일반주제명
- Computer engineering
- 키워드
- Machine learning
- 기타저자
- University of California, Los Angeles Computer Science 0201
- 기본자료저록
- Dissertations Abstracts International. 85-12B.
- 전자적 위치 및 접속
- 로그인 후 원문을 볼 수 있습니다.
MARC
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■00520250211152003
■006m o d
■007cr#unu||||||||
■020 ▼a9798382829708
■035 ▼a(MiAaPQ)AAI31330224
■040 ▼aMiAaPQ▼cMiAaPQ
■0820 ▼a004
■1001 ▼aCao, Yadi.
■24510▼aAdvancing Physics-Based Simulations: Integrating Conventional and Machine-Learning Approaches for Enhanced Computational Efficiency
■260 ▼a[Sl]▼bUniversity of California, Los Angeles▼c2024
■260 1▼aAnn Arbor▼bProQuest Dissertations & Theses▼c2024
■300 ▼a129 p
■500 ▼aSource: Dissertations Abstracts International, Volume: 85-12, Section: B.
■500 ▼aAdvisor: Terzopoulos, Demetri;Jiang, Chenfanfu;Terzopoulos, Demetri.
■5021 ▼aThesis (Ph.D.)--University of California, Los Angeles, 2024.
■520 ▼aThis thesis presents novel approaches to improve the accuracy and efficiency of scientific simulations, particularly those involving complex geometries, intrinsic physical modeling, and demanding computational costs.The first contribution extends the MPM to unstructured meshes, addressing the challenges of the transfer kernel's gradient continuity and stability issue on any general mesh tesselation. The Unstructured Moving Least Squares MPM (UMLS-MPM) incorporates a diminishing function into the MLS kernel's sample weights, ensuring an analytically continuous function and gradient reconstruction. It is the first-of-its-kind framework in this field. Several numerical test cases demonstrate the method's stability and accuracy.The second contribution is a hybrid scheme for modeling the interaction between compressible flow, shock waves, and deformable structures. By combining recent advancements in time-splitting compressible flow and Material Point Methods (MPMs), this approach seamlessly integrates Eulerian and Lagrangian/Eulerian methods for monolithic flow-structure interactions. Reflective and penetrable boundary conditions handle deforming boundaries with sub-cell particles, while a mixed-order finite element formulation utilizing B-spline shape functions discretizes the coupled velocity-pressure system. This comprehensive framework accurately captures shock wave propagation, temperature/density-induced buoyancy effects, and topology changes in solids.The third contribution addresses challenges in learning physical simulations on largescale meshes using Graph Neural Networks (GNNs). Existing state-of-the-art methods often encounter issues related to over-smoothing and incorrect edge construction during multi-scale adaptation. To overcome these limitations, a novel pooling strategy, termed bi-stride, is introduced. This approach, inspired by bipartite graph structures, involves pooling nodes on alternate frontiers of the breadth-first search (BFS), eliminating the need for labor-intensive manual creation of coarser meshes and mitigating incorrect edge problems. The proposed BSMS-GNN framework employs non-parametrized pooling and unpooling through interpolations, resulting in a substantial reduction of computational costs and improved efficiency. Experimental results demonstrate the superiority of the BSMS-GNN framework in terms of both accuracy and computational efficiency in representative physical simulations on large-scale meshes.
■590 ▼aSchool code: 0031.
■650 4▼aComputer science
■650 4▼aEngineering
■650 4▼aComputer engineering
■653 ▼aGraph Neural Networks
■653 ▼aMachine learning
■653 ▼aMaterial Point Methods
■653 ▼aPhysics based simulation
■653 ▼aBreadth-first search
■690 ▼a0984
■690 ▼a0537
■690 ▼a0464
■71020▼aUniversity of California, Los Angeles▼bComputer Science 0201.
■7730 ▼tDissertations Abstracts International▼g85-12B.
■790 ▼a0031
■791 ▼aPh.D.
■792 ▼a2024
■793 ▼aEnglish
■85640▼uhttp://www.riss.kr/pdu/ddodLink.do?id=T17162364▼nKERIS▼z이 자료의 원문은 한국교육학술정보원에서 제공합니다.
