Automating optimisation subject to partial differential equations on high-performance computers.

在高性能计算机上根据偏微分方程自动优化。

基本信息

  • 批准号:
    EP/K030930/1
  • 负责人:
  • 金额:
    $ 62.08万
  • 依托单位:
  • 依托单位国家:
    英国
  • 项目类别:
    Fellowship
  • 财政年份:
    2013
  • 资助国家:
    英国
  • 起止时间:
    2013 至 无数据
  • 项目状态:
    已结题

项目摘要

Optimisation problems appear across all areas of engineering. Optimisation consists of maximising the performance or minimising the cost of a system, subject to some constraints. For example, an aeronautical engineer will want to choose the best shape for a wing to maximise its efficiency, subject to the constraint that the wing will lift the aircraft, while a civil engineer will want to design the cheapest bridge that will support its load. An important class of optimisation problems is where the constraint is given by the laws of physics, such as the physical laws for fluids (in the wing case) and structures (in the bridge case). These problems can be very hard, and usually require massive supercomputers to solve them. A significant amount of mathematical research has gone into investigating techniques for solving them.Engineers currently face a major practical difficulty when trying to solve new kinds of such optimisation problems. The software required to solve these is very intricate, and often takes months or years to develop. This poses a very formidable barrier. This matters a lot, because these problems appear everywhere in engineering, and if we could solve them then we could design many things in a better way.I propose to do this by developing a software framework to generate optimisation codes, rather than have engineers develop them by hand. While the optimisation software is very complex, it has a compact mathematical structure: I propose to generate the optimisation software from a simple high-level description of this mathematical structure. By generating the necessary software, engineers can spend their time on using it to solve real problems. This framework will provide engineers with the necessary optimisaton software in days or weeks instead of months or years.Generating the optimisation codes from simple high-level input has another major advantage. The high-performance supercomputers necessary to solve these optimisation problems are extremely difficult to program efficiently, and are changing rapidly. Code must be tailored for a particular hardware architecture. As each new kind of computing platform comes out, an engineer must adapt the code. Instead, with my new approach, the engineer can simply re-generate the code from the same mathematical input, and the framework will specialise the code to best exploit the different platform. By updating the framework once, many engineers working on many different codes in many different areas can benefit quickly from advances in computational hardware.I will apply the software developed to two important engineering problems. The first engineering problem is found in the design of marine turbine farms for renewable energy. Marine renewable energy is very important to the UK. The government predicts that the industry will be worth £76 billion to the UK economy by 2050. A major problem facing the industry is how to position the turbines to extract the maximum possible energy from the tide. Choosing the best design is very important, as it can greatly change the efficiency. Solving this problem will directly contribute to the UK's energy security and carbon reduction goals.The second engineering problem is identifying regions of the heart that are damaged (ischaemic). Ischaemic heart disease is the most common cause of death in Western countries. When a doctor suspects that a patient has ischaemia, it would be very beneficial to know its location and extent. One possible approach to rapidly identify ischaemia is to extract information from electrocardiograms (ECGs). The optimisation problem is to identify the ischaemia that best explains the ECG measured from the patient. Solving this problem will directly contribute to better healthcare decisions, reducing the mortality rate and improving the long-term prognosis of survivors.
优化问题出现在工程的所有领域。优化包括最大化系统的性能或最小化系统的成本,并受到一些约束。例如,航空工程师会希望选择机翼的最佳形状,以最大限度地提高其效率,但要受到机翼将提升飞机的约束,而土木工程师则希望设计最便宜的桥梁来支撑其负载。一类重要的优化问题是约束由物理定律给出,例如流体(在机翼情况下)和结构(在桥梁情况下)的物理定律。这些问题可能非常困难,通常需要大型超级计算机来解决。大量的数学研究已经投入到研究解决这些问题的技术中。工程师们目前在试图解决新类型的此类优化问题时面临着一个主要的实际困难。解决这些问题所需的软件非常复杂,通常需要几个月或几年的时间来开发。这构成了一个非常可怕的障碍。这很重要,因为这些问题在工程中无处不在,如果我们能解决它们,那么我们就能以更好的方式设计许多东西。我建议通过开发一个软件框架来生成优化代码,而不是让工程师手工开发它们。虽然优化软件是非常复杂的,它有一个紧凑的数学结构:我建议从这个数学结构的一个简单的高级描述生成优化软件。通过生成必要的软件,工程师可以花时间使用它来解决真实的问题。该框架将在几天或几周内为工程师提供必要的优化软件,而不是几个月或几年。从简单的高级输入生成优化代码还有另一个主要优点。解决这些优化问题所需的高性能超级计算机非常难以有效编程,并且变化迅速。代码必须为特定的硬件架构量身定制。随着每一种新的计算平台的出现,工程师必须调整代码。相反,通过我的新方法,工程师可以简单地从相同的数学输入重新生成代码,并且框架将专门化代码以最好地利用不同的平台。通过对框架进行一次更新,在许多不同领域中从事许多不同代码工作的许多工程师可以迅速受益于计算硬件的进步。第一个工程问题是可再生能源的海洋涡轮机农场的设计。海洋可再生能源对英国非常重要。政府预测,到2050年,该行业将为英国经济带来760亿英镑的价值。该行业面临的一个主要问题是如何定位涡轮机,以从潮汐中提取最大可能的能量。选择最佳设计非常重要,因为它可以大大改变效率。解决这个问题将直接有助于英国的能源安全和碳减排目标。第二个工程问题是识别心脏受损(缺血)的区域。缺血性心脏病是西方国家最常见的死亡原因。当医生怀疑患者患有缺血时,了解其位置和程度将非常有益。快速识别缺血的一种可能方法是从心电图(ECG)中提取信息。优化问题是识别最能解释从患者测量的ECG的缺血。解决这个问题将直接有助于更好的医疗决策,降低死亡率,改善幸存者的长期预后。

项目成果

期刊论文数量(10)
专著数量(0)
科研奖励数量(0)
会议论文数量(0)
专利数量(0)
Efficient white noise sampling and coupling for multilevel Monte Carlo with non-nested meshes
具有非嵌套网格的多级蒙特卡罗的高效白噪声采样和耦合
  • DOI:
    10.48550/arxiv.1803.04857
  • 发表时间:
    2018
  • 期刊:
  • 影响因子:
    0
  • 作者:
    Croci M
  • 通讯作者:
    Croci M
Computing stationary solutions of the two-dimensional Gross-Pitaevskii equation with deflated continuation
Combining Deflation and Nested Iteration for Computing Multiple Solutions of Nonlinear Variational Problems
结合紧缩和嵌套迭代计算非线性变分问题的多重解
Geometric MCMC for infinite-dimensional inverse problems
  • DOI:
    10.1016/j.jcp.2016.12.041
  • 发表时间:
    2017-04-15
  • 期刊:
  • 影响因子:
    4.1
  • 作者:
    Beskos, Alexandros;Girolami, Mark;Stuart, Andrew M.
  • 通讯作者:
    Stuart, Andrew M.
Analysis of Carrier's problem
运营商问题分析
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Patrick Farrell其他文献

The barriers and facilitators to the design and delivery of sustainable healthcare across NHS Scotland: A forcefield analysis
  • DOI:
    10.1016/j.fhj.2024.100135
  • 发表时间:
    2024-06-01
  • 期刊:
  • 影响因子:
  • 作者:
    Alice Harpur;Kirsty Crowe;Emily Turner;Katherine Jobling;Luis Loureiro Harrison;Gary Paul;Richard Tran;Patrick Farrell
  • 通讯作者:
    Patrick Farrell
CARLETON UNIVERSITY SCHOOL OF MATHEMATICS AND STATISTICS HONOURS PROJECT TITLE: Model-Based Estimation for a Relative Difference in Proportions
卡尔顿大学数学与统计学荣誉项目名称:基于模型的比例相对差异估计
  • DOI:
  • 发表时间:
    2019
  • 期刊:
  • 影响因子:
    0
  • 作者:
    Brianne Rogers;Patrick Farrell
  • 通讯作者:
    Patrick Farrell

Patrick Farrell的其他文献

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{{ truncateString('Patrick Farrell', 18)}}的其他基金

SysGenX: Composable software generation for system-level simulation at Exascale
SysGenX:用于百亿亿次系统级仿真的可组合软件生成
  • 批准号:
    EP/W026163/1
  • 财政年份:
    2021
  • 资助金额:
    $ 62.08万
  • 项目类别:
    Research Grant
A new simulation and optimisation platform for marine technology
全新的海洋技术仿真和优化平台
  • 批准号:
    EP/M011151/1
  • 财政年份:
    2015
  • 资助金额:
    $ 62.08万
  • 项目类别:
    Research Grant
Doctoral Dissertation Research: A description of the Patwin language
博士论文研究:Patwin 语言的描述
  • 批准号:
    1264305
  • 财政年份:
    2013
  • 资助金额:
    $ 62.08万
  • 项目类别:
    Standard Grant
Lehigh ADVANCE: Building Community Beyond Academic Departments
Lehigh ADVANCE:建立学术部门之外的社区
  • 批准号:
    1008375
  • 财政年份:
    2010
  • 资助金额:
    $ 62.08万
  • 项目类别:
    Cooperative Agreement
Wisconsin Alliance for Minority Participation
威斯康星州少数族裔参与联盟
  • 批准号:
    0402549
  • 财政年份:
    2004
  • 资助金额:
    $ 62.08万
  • 项目类别:
    Cooperative Agreement
Particle Sizing Using Particle Image Velocimetry
使用粒子图像测速法测定粒子大小
  • 批准号:
    9106568
  • 财政年份:
    1991
  • 资助金额:
    $ 62.08万
  • 项目类别:
    Continuing Grant
Research Initiation: Heat and Mass Transfer From Solid Combustible Particles
研究启动:固体可燃颗粒的传热传质
  • 批准号:
    8307863
  • 财政年份:
    1983
  • 资助金额:
    $ 62.08万
  • 项目类别:
    Standard Grant

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Structure-guided optimisation of light-driven microalgae cell factories
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