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Efficient Parallel Computation of Particle-Based Biological Systems: Applications to Simulations of Bird Flocks and Optimizing Models of Cardiac Tissue
Efficient Parallel Computation of Particle-Based Biological Systems: Applications to Simulations of Bird Flocks and Optimizing Models of Cardiac Tissue
상세정보
- 자료유형
- 학위논문 서양
- 최종처리일시
- 20260202105503
- ISBN
- 9798263324902
- DDC
- 001
- 서명/저자
- Efficient Parallel Computation of Particle-Based Biological Systems: Applications to Simulations of Bird Flocks and Optimizing Models of Cardiac Tissue
- 발행사항
- [Sl] : Georgia Institute of Technology, 2025
- 발행사항
- Ann Arbor : ProQuest Dissertations & Theses, 2025
- 형태사항
- 185 p
- 주기사항
- Source: Dissertations Abstracts International, Volume: 87-06, Section: B.
- 주기사항
- Advisor: Cherry, Elizabeth.
- 학위논문주기
- Thesis (Ph.D.)--Georgia Institute of Technology, 2025.
- 초록/해제
- 요약Graphics processing units (GPUs) are ubiquitous in modern computers, and are characterized by their large-scale parallel computing capability. Many computing problems that require updating state variables for a large number of mostly independent agents are well-suited to simulation using GPUs, as the steps that can be parallelized often represent the majority of the computation but are individually simple. This thesis focuses on two particle-based systems which have been implemented using this approach. In both cases, these simulations use the Web Graphics Library (WebGL) available in all modern web browsers to utilize any available graphics hardware without requiring any manual software installation, configuration, or compilation by the end-user. The first program uses particle swarm optimization (PSO), an optimization technique inspired by the behavior of groups of animals, to identify parameterizations of cardiac models to match experimental data. This tool, called CardioFit, allows the identification of parameters in a matter of seconds on most computers and is capable of finding low-error solutions even for challenging datasets, such as data recorded from human hearts exhibiting Brugada syndrome. By design, CardioFit is easy to use through a user interface without requiring any code to be written or modified by the end-user, while still allowing control over optimization parameters for advanced users. The second program simulates flocks of up to tens of thousands of birds in near-real time, with interactive settings to choose between a variety of flocking models, change their parameters, and observe the behavior through a live visualization. This flocking simulation reveals that large flocks of birds place greater constraints on existing flocking models to obtain desired behaviors such as the ability to maintain the flock through turns due to the need for long-range information transfer.
- 일반주제명
- Software
- 일반주제명
- Physiology
- 일반주제명
- Computer science
- 기본자료저록
- Dissertations Abstracts International. 87-06B.
- 전자적 위치 및 접속
- 로그인 후 원문을 볼 수 있습니다.
MARC
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■020 ▼a9798263324902
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■035 ▼a(MiAaPQ)GeorgiaTech78722
■040 ▼aMiAaPQ▼cMiAaPQ
■0820 ▼a001
■1001 ▼aComstock, Maxfield Roth.
■24510▼aEfficient Parallel Computation of Particle-Based Biological Systems: Applications to Simulations of Bird Flocks and Optimizing Models of Cardiac Tissue
■260 ▼a[Sl]▼bGeorgia Institute of Technology▼c2025
■260 1▼aAnn Arbor▼bProQuest Dissertations & Theses▼c2025
■300 ▼a185 p
■500 ▼aSource: Dissertations Abstracts International, Volume: 87-06, Section: B.
■500 ▼aAdvisor: Cherry, Elizabeth.
■5021 ▼aThesis (Ph.D.)--Georgia Institute of Technology, 2025.
■520 ▼aGraphics processing units (GPUs) are ubiquitous in modern computers, and are characterized by their large-scale parallel computing capability. Many computing problems that require updating state variables for a large number of mostly independent agents are well-suited to simulation using GPUs, as the steps that can be parallelized often represent the majority of the computation but are individually simple. This thesis focuses on two particle-based systems which have been implemented using this approach. In both cases, these simulations use the Web Graphics Library (WebGL) available in all modern web browsers to utilize any available graphics hardware without requiring any manual software installation, configuration, or compilation by the end-user. The first program uses particle swarm optimization (PSO), an optimization technique inspired by the behavior of groups of animals, to identify parameterizations of cardiac models to match experimental data. This tool, called CardioFit, allows the identification of parameters in a matter of seconds on most computers and is capable of finding low-error solutions even for challenging datasets, such as data recorded from human hearts exhibiting Brugada syndrome. By design, CardioFit is easy to use through a user interface without requiring any code to be written or modified by the end-user, while still allowing control over optimization parameters for advanced users. The second program simulates flocks of up to tens of thousands of birds in near-real time, with interactive settings to choose between a variety of flocking models, change their parameters, and observe the behavior through a live visualization. This flocking simulation reveals that large flocks of birds place greater constraints on existing flocking models to obtain desired behaviors such as the ability to maintain the flock through turns due to the need for long-range information transfer.
■590 ▼aSchool code: 0078.
■650 4▼aSoftware
■650 4▼aPhysiology
■650 4▼aComputer science
■653 ▼aGraphics processing units
■653 ▼aParticle swarm optimization
■690 ▼a0984
■690 ▼a0719
■71020▼aGeorgia Institute of Technology.
■7730 ▼tDissertations Abstracts International▼g87-06B.
■790 ▼a0078
■791 ▼aPh.D.
■792 ▼a2025
■793 ▼aEnglish
■85640▼uhttp://www.riss.kr/pdu/ddodLink.do?id=T17360297▼nKERIS▼z이 자료의 원문은 한국교육학술정보원에서 제공합니다.
