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RANDOMNESS: A RESOURCE FOR REAL-TIME ANALYTICS

随机性：实时分析资源

基本信息

批准号：
EP/R041431/1
负责人：
Nicholas Polydorides
金额：
$ 29.3万
依托单位：
University of Edinburgh
依托单位国家：
英国
项目类别：
Research Grant
财政年份：
2018
资助国家：
英国
起止时间：
2018 至无数据
项目状态：
已结题

来源：
https://gtr.ukri.org/projects?ref=EP%2FR041431%2F1
关键词：
RANDOMNESS RESOURCE REAL TIME ANALYTICS

项目摘要

The scope: Modern engineering relies on data and models to broaden our understanding of complex systems, devices and processes, through predictive and diagnostic analytics. Examples of this include fluid dynamic simulations for energy conversion, electromagnetic models in geophysical and environmental monitoring, mechanics in design of resilient infrastructures, acoustic and X-ray models for non-destructive testing and optical models in biomedical imaging. Traditionally, numerical computing has been at the forefront of engineering, however its embedding within the engineering process is still hindered by the complexity associated with realistic data models. Currently, process analytics, operate either off-line, on high performance computing infrastructure for accurate simulations and sophisticated data processing algorithms, or in real-timebased on oversimplified problem specifications that yield some crude imperative information.The challenge:To empower data centric engineering in manufacturing and quality assurance processes with real-time, accurate modelling and data processing we take on the challenge of real-time, large-scale computing, by replacing the conventional way we perform algebraic computations with a more efficient randomised scheme. In the context of basic solution of linear equations for example, this approach randomly selects a small fraction of the elements in the matrices and the vectors involved, radically reducing the computational effort and time. What's more impressive than this, is that when optimally sampled, this computational efficiency is also complemented by a very small solution error, and thus by investigating ways that we can compute these optimal sampling distributions we can achieve massive computational savings, ultimately providing the productive sectors of the economy with an affordable solution for real-time modelling and data processing, without compromising the quality and accuracy of the sought information.Main objectives:The main objective of this project is to develop a new form of the popular finite element method by incorporating algorithms for randomised linear algebra. Through theory, analysis and computation we seek to prove a concept of randomised finite element method for simulating diffusion processes and solving the associated inverse data-fitting problems by investigating how the respective optimal sampling distributions can be computed and sampled in an efficient way.Why does it matter?The success of this project will make a measurable contribution on making accurate, high-dimensional computing portable and affordable to the broad engineering and manufacturing sector, allowing for real-time process monitoring and control even where high performance computing infrastructure is not available.What difference will it achieve?Our novel framework of data analytics aims to provide prompt and accurate insights into complex and dynamic data and models. In a manufacturing process this will lead to a rise in productivity, monitoring quality of services and products, as well as reduction of operational costs and waste. We also foresee that these advances will find application in the broader engineering sector as well as having an impact health informatics to enable simultaneous imaging and therapy for cancer patients and national security in being able to detect and screen in real time against threads.

范围：现代工程依赖于数据和模型，通过预测和诊断分析来扩大我们对复杂系统、设备和流程的理解。这方面的例子包括用于能量转换的流体动力学模拟、地球物理和环境监测中的电磁模型、弹性基础设施设计中的力学、用于无损检测的声学和X射线模型以及生物医学成像中的光学模型。传统上，数值计算一直处于工程的最前沿，但其在工程过程中的嵌入仍然受到与现实数据模型相关的复杂性的阻碍。目前，过程分析要么离线运行，在高性能计算基础设施上进行精确的模拟和复杂的数据处理算法，要么实时运行，基于过于简化的问题规范，产生一些粗略的必要信息。为了在制造和质量保证过程中实现以数据为中心的工程，我们接受了实时、大规模计算的挑战，通过用更有效的随机化方案代替常规方式，我们执行代数计算。例如，在线性方程组的基本解的上下文中，这种方法随机选择矩阵中的一小部分元素和所涉及的向量，从根本上减少了计算工作量和时间。比这更令人印象深刻的是，当最佳采样时，这种计算效率还得到了非常小的解决方案误差的补充，因此通过研究我们可以计算这些最佳采样分布的方法，我们可以实现大量的计算节省，最终为经济的生产部门提供负担得起的实时建模和数据处理解决方案，主要目标：本项目的主要目标是通过结合随机线性代数的算法，开发一种新形式的流行有限元法。通过理论，分析和计算，我们试图证明一个概念的随机有限元方法模拟扩散过程和解决相关的逆数据拟合问题，通过调查如何各自的最佳采样分布可以计算和采样在一个有效的方式。该项目的成功将为广泛的工程和制造部门提供精确的、可移植的和可负担的高维计算，即使在没有高性能计算基础设施的情况下也可以进行实时过程监控和控制。我们新颖的数据分析框架旨在为复杂和动态的数据和模型提供及时和准确的见解。在制造过程中，这将导致生产力的提高，监控服务和产品的质量，以及减少运营成本和浪费。我们还预见，这些进步将在更广泛的工程领域中得到应用，并对健康信息学产生影响，使癌症患者和国家安全能够同时成像和治疗，能够在真实的时间内检测和筛选线程。