Learn2Sim: Learning to steer computer simulators

计算机模拟器是物理、社会和生物科学中的一种流行工具。它们是使用系统组件如何相互作用的学科领域知识构建的。通常，这些交互被建模为随机的，并且涉及模拟器代码生成随机数以决定结果。模拟器可用于在不同场景下进行模拟，以便深入了解整个系统的行为。一个示例应用是高能物理实验，这是该项目的重点。在这里，模拟器计算发生的大量随机粒子相互作用的结果。大规模模拟器在当代的其他重要应用包括：行星气候模型;流行病学模型，既能捕捉疾病在人群中的传播，又能捕捉导致疾病的病毒或细菌的基因变化;由个体因素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。这涉及到寻找其参数的合理值：控制模拟器行为的数值，其确切值事先未知。事实上，确定这些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断。现有的计算机模拟器统计推断方法大多涉及在候选参数值下运行模拟，直到发现输出与观测数据之间的紧密匹配。然而，对于大规模的模拟器，观察到的数据通常是复杂的，所以很难接近匹配。此外，每个模拟器运行在计算上是昂贵的。因此，现有的模拟器的统计推断方法仅限于小规模的模拟器。另一个限制是，它们产生的近似结果的可信度是很难量化的。该项目将使用一种新的方法来学习如何“引导”计算机模拟器的随机组件，使每次运行产生一个密切匹配的观察数据。然后，可以使用概率理论计算特定参数值的可匹配性，其基于它产生的匹配程度，以及在没有转向的情况下所需的随机行为模式的可能性。这是一个具有挑战性的目标，因为模拟器的随机组件通常很大，复杂且难以建模。为了实现这一目标，该项目将联合收割机，并扩大令人兴奋的最新进展，在计算统计和机器learning.To实现这种方法在实践中，我们将使用一个国家的最先进的“概率编程语言”开发的项目合作者取代随机数生成过程的模拟器与导向过程。这允许在不修改的情况下重用模拟器代码，当存在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制作通用软件，以释放大规模计算机模拟器与数据拟合的潜力。还计划将其应用于高能粒子物理学中的一个特定应用：利用大型强子对撞机数据上的τ轻子衰变“夏尔巴”模拟器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.