PRISM: Platform for Research In Simulation Methods

计算科学是一门跨学科的研究，涵盖了应用数学、计算机科学和工程，以及来自科学、技术和医学等应用领域的投入。先进的模拟方法不仅有可能彻底改变科学研究，也有可能改变工业经济，为公司的产品提供竞争优势，提高生产力，并为创造性的探索和创新提供环境。计算科学包含了大量的主题，这意味着这个领域是广阔的，新的方法不断被发表。这些方法不仅涉及到核心的仿真技术，而且涉及到依赖于仿真的问题。这些问题包括量化不确定性（即要求误差条），将模型与数据混合以做出更好的预测，解决逆问题（如果输出是Y，输入是X?），以及优化设计（例如找到最符合空气动力学的车辆形状）。不幸的是，先进的新方法进入应用和工业实践的过程非常缓慢。其中一个原因是，将数学算法应用于复杂的仿真模型非常具有侵入性；大多数情况下，他们不能将模拟代码视为“黑盒”。它们通常需要重写软件，这是非常耗时和昂贵的。在我们的研究中，我们通过使用自动化生成计算机模拟代码来解决这个问题。关键思想是，模拟算法以某种抽象的方式描述（在仔细考虑了关键方面之后，它看起来尽可能像底层数学），并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改变时（例如正在使用一种新型计算机），可以使用这些工具从抽象描述中重建代码。这种灵活性极大地加速了高级算法在现实世界问题中的应用。考虑一下优化一级方程式赛车形状以最小化其阻力的例子。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，并且能够在每次迭代中修改模拟中使用的形状。通常，这种侵入性需要对模拟软件进行大量修改。但是通过存储空气动力学方程的符号表示，所有优化所需的操作都可以在我们的系统中生成，而无需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推广这种方法，并生产公开可用的、可持续的开源软件，以确保其被采用。该平台将使我们能够在软件方法上取得进步，使我们能够继续在我们工作的广泛应用领域获得工业和政府资金，包括航空航天和汽车行业、可再生能源、医药和外科、环境和制造业。

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.