PRISM: Platform for Research In Simulation Methods

PRISM：仿真方法研究平台

基本信息

批准号：
EP/R029423/1
负责人：
Spencer Sherwin
金额：
$ 205.52万
依托单位：
Imperial College London
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%2FR029423%2F1
关键词：
PRISM Platform Research Simulation Methods

项目摘要

Computational science is a multidisciplinary research endeavour spanning applied mathematics, computer science and engineering together with input from application areas across science, technology and medicine. Advanced simulation methods have the potential to revolutionise not only scientific research but also to transform the industrial economy, offering companies a competitive advantage in their products, better productivity, and an environment for creative exploration and innovation.The huge range of topics that computational science encapsulates means that the field is vast and new methods are constantly being published. These methods relate not only to the core simulation techniques but also to problems which rely on simulation. These problems include quantifying uncertainty (i.e. asking for error bars), blending models with data to make better predictions, solving inverse problems (if the output is Y, what is the input X?), and optimising designs (e.g. finding a vehicle shape that is the most aerodynamic). Unfortunately, the process through which advanced new methods find their way into applications and industrial practice is very slow.One of the reasons for this is that applying mathematical algorithms to complex simulation models is very intrusive; mostly they cannot treat the simulation code as a "black box". They often require rewriting of the software, which is very time consuming and expensive. In our research we address this problem by using automating the generation of computer code for simulation. The key idea is that the simulation algorithm is described in some abstract way (which looks as much like the underlying mathematics as possible, after thinking carefully about what the key aspects are), and specialised software tools are used to automatically build the computer code. When some aspect of the implementation needs to change (for example a new type of computer is being used) then these tools can be used to rebuild the code from the abstract description. This flexibility dramatically accelerates the application of advanced algorithms to real-world problems.Consider the example of optimising the shape of a Formula 1 car to minimise its drag. The optimisation process is highly invasive: it must solve auxiliary problems to learn how to improve the design, and it be able to modify the shape used in the simulation at each iteration. Typically this invasiveness would require extensive modifications to the simulation software. But by storing a symbolic representation of the aerodynamic equations, all operations necessary for the optimisation can be generated in our system, without needing to rewrite or modify the aerodynamics code at all.The research goal of our platform is to investigate and promote this methodology, and to produce publicly available, sustainable open-source software that ensures its uptake. The platform will allow us to make advances in our software approach that enables us to continue to secure industrial and government funding in the broad range of application areas we work in, including aerospace and automotive sectors, renewable energy, medicine and surgery, the environment, and manufacturing.

计算科学是一项多学科研究，跨越了应用数学，计算机科学和工程以及科学，技术和医学跨应用领域的投入。先进的仿真方法不仅可以改变科学研究，而且可以改变工业经济，从而为公司提供竞争优势，更好的生产率以及创造性探索和创新的环境。计算科学封装的大量主题意味着该领域是广阔的和新的方法，并不断发表新的方法。这些方法不仅与核心仿真技术有关，而且与依赖仿真的问题有关。这些问题包括量化不确定性（即要求错误栏），将模型与数据融合以做出更好的预测，解决反问题（如果输出为y，输入x？）以及优化设计（例如，找到最空气动力学的车辆形状）。不幸的是，高级新方法进入应用程序和工业实践的过程非常慢。这样做的原因之一是，将数学算法应用于复杂的模拟模型是非常侵入性的。通常，他们无法将仿真代码视为“黑匣子”。他们通常需要重写该软件，这非常耗时且昂贵。在我们的研究中，我们通过自动使用计算机代码进行仿真来解决此问题。关键的想法是，在仔细考虑关键方面是什么之后，以某种抽象的方式描述了仿真算法（看起来与潜在的数学一样非常类似），并且使用专门的软件工具来自动构建计算机代码。当实现的某些方面需要更改（例如使用新型计算机）时，这些工具可用于从摘要描述中重建代码。这种灵活性极大地加速了高级算法在现实世界中的应用。请考虑优化一级方程式1辆车的形状以最小化其阻力的示例。优化过程是高度侵入性的：它必须解决辅助问题，以学习如何改进设计，并且能够修改每次迭代中模拟中使用的形状。通常，这种侵入性需要对模拟软件进行广泛的修改。但是，通过存储空气动力学方程的符号表示，可以在我们的系统中生成优化所需的所有操作，而无需完全重写或修改空气动力学代码。我们平台的研究目标是调查和促进这种方法，并生成公开可用的可用开放式软件，以确保其效果。该平台将使我们能够在软件方法中进步，使我们能够继续在我们工作的广泛应用领域中获得工业和政府资金，包括航空航天和汽车领域，可再生能源，医学和手术，环境以及制造业。