Learn2Sim: Learning to steer computer simulators

计算机模拟器是物理、社会和生物科学中流行的工具。它们是使用关于系统组件如何相互作用的主题领域知识来构建的。通常，这些交互被建模为随机的，并且涉及生成随机数的模拟器代码来决定结果。模拟器可以用来在不同的场景下进行模拟，以提供对整个系统行为的洞察。本项目的重点是高能物理实验。在这里，模拟器计算发生的大量随机粒子相互作用的结果。大型模拟器在当代的其他重要应用包括：行星气候模型；捕捉疾病在人群中传播和导致疾病的病毒或细菌的基因变化的流行病学模型；由个体代理人组成的金融和经济模型。为了有效，模拟器必须调整到与观测数据相匹配。这涉及到找到它们参数的合理值：控制模拟器行为的数值，其确切值事先是未知的。事实上，确定这些价值可能是主要的科学目标。例如，在物理应用中，参数可以包括未知粒子质量的值。学习参数值的问题被称为统计推理。现有的计算机仿真器的统计推理方法大多是在候选参数值下运行仿真，直到在输出和观测数据之间找到紧密的匹配。然而，对于大型模拟器，观测数据通常很复杂，因此很少有接近匹配的情况。此外，每次模拟器运行的计算代价都很高。因此，现有的仿真器统计推理方法仅限于小型仿真器。另一个限制是它们产生的结果近似，其可信度很难量化。该项目将使用一种新的方法来学习如何“操纵”计算机模拟器的随机分量，以便每次运行都产生与观测数据密切匹配的结果。然后，可以使用概率理论来计算特定参数值的可信程度，该理论基于它产生的匹配有多接近，以及所需的随机行为模式在没有转向的情况下发生的可能性有多大。这是一个具有挑战性的目标，因为模拟器的随机分量通常很大、很复杂并且很难建模。为了实现这一点，该项目将结合和扩展计算统计学和机器学习方面令人兴奋的最新进展。为了在实践中实施这一方法，我们将使用由项目合作者开发的最先进的“概率编程语言”，它用引导过程取代模拟器的随机数生成过程。这允许在不修改的情况下重复使用模拟器代码，当有详细的预先存在的模拟器代码时，这是一个相当大的好处。该项目的一个计划产出是为我们的统计推断方法生产通用软件，以释放使大型计算机模拟器适应数据的潜力。它还计划将其应用于高能粒子物理中的一个特殊应用：使用大型强子对撞机数据上的tau轻子衰变的“Sherpa”模拟器来探索希格斯玻色子的性质。

Computer simulators are a popular tool in physical, social and biological sciences. They are constructed using subject area knowledge of how the components of a system interact with each other. Often these interactions are modelled as being random, and involve the simulator code generating random numbers to decide the outcome. A simulator can be used to perform simulations under different scenarios in order to provide insights into the overall system behaviour.One example application, which is the focus of this project, is to high energy physics experiments. Here the simulator calculates outcomes of the large number of random particle interactions which take place. Other important contemporary applications of large scale simulators include: models of planetary climate; epidemiological models capturing both disease spread in a population and genetic changes in the virus or bacteria causing the disease; financial and economic models made up of individual agents.To be effective, simulators must be tuned to match observed data. This involves finding plausible values of their parameters: numerical values which control the behaviour of the simulator and whose exact values are unknown in advance. Indeed, determining these values may be the principal scientific goal. For instance in a physics application, the parameters could include the values of unknown particle masses. The problem of learning parameter values is referred to as statistical inference.Existing approaches to statistical inference for computer simulators mostly involve running simulations under candidate parameter values until close matches are found between outputs and observed data. However for large scale simulators, the observed data is typically complex so that close matches are rare. Also each simulator run is computationally expensive. Therefore existing statistical inference methods for simulators are limited to small scale simulators. Another limitation is that they produce approximate results whose trustworthiness is hard to quantify.The project will use a novel approach of learning how to "steer" the random components of computer simulators so that each run produces a close match to the observed data. The plausibility of particular parameter values can then be calculated using probability theory based on how close a match it produces, and how likely the required pattern of random behaviour would have been without steering. This is a challenging goal as a simulator's random components are often large, complex and hard to model. To achieve it the project will combine and extend exciting recent advances in both computational statistics and machine learning.To implement this approach in practice we will use a state-of-the-art "probabilistic programming language" developed by a project collaborator which replaces the random number generation process of the simulator with the steered process. This allows the reuse of simulator code without modification, a considerable benefit when there is detailed pre-existing simulator code. One planned output of the project is to produce general-purpose software for our approach to statistical inference, in order to unlock the potential of fitting large scale computer simulators to data. It is also planned to apply this to a particular application in high energy particle physics: exploring properties of the Higgs boson using the "Sherpa" simulator of tau lepton decay on Large Hadron Collider data.