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Discovering rare, extreme behaviour in large-scale computational models

发现大规模计算模型中罕见的极端行为

基本信息

批准号：
MR/T041862/1
负责人：
Eloisa Bentivegna
金额：
$ 140.94万
依托单位：
IBM (United Kingdom)
依托单位国家：
英国
项目类别：
Fellowship
财政年份：
2021
资助国家：
英国
起止时间：
2021 至无数据
项目状态：
未结题

来源：
https://gtr.ukri.org/projects?ref=MR%2FT041862%2F1
关键词：
Discovering rare extreme behaviour large

项目摘要

The construction of high-fidelity digital models of complex physical phenomena, and more importantly their deployment as investigation tools for science and engineering, are some of the most critical undertakings of scientific computing today. Without computational models, the study of spatially-irregular, multi-scale, or highly coupled, nonlinear physical systems would simply not be tractable.Even when computational models are available, however, tuning their physical and geometrical parameters (sometimes referred to as control variables) for optimal exploration and discovery is a colossal endeavour. In addition to the technological challenges inherent to massively parallel computation, the task is complicated by the scientific complexity of large-scale systems, where many degrees of freedom can team up and generate emergent, anomalous, resonant features which get more and more pronounced as the model's fidelity is increased (e.g., in turbulent scenarios). These features may correspond to highly interesting system configurations, but they are often too short-lived or isolated in the control space to be found using brute-force computation alone. Yet, most computational surveys today are guided by random (albeit somewhat educated by instinct) guesses.The potential for missed phenomenology is simply unquantifiable. In many domains, anomalous solutions could describe life-threatening events such as extreme weather. A digital model of an industrial system may reveal, under special conditions, an anomalous response to the surrounding environment, which could lead to decreased efficiency, material fatigue, and structural failure. Precisely because of their singular and catastrophic nature, as well as infrequency and short life, these configurations are also the hardest to predict. Any improvement in our capacity to locate where anomalous dynamics may unfold could therefore tremendously impact our ability to protect against extreme events. More fundamentally, establishing whether the set of equations implemented in a computational model is at all able to reproduce specific, exotic solutions (such as rare astronomical transients [1]) for certain configuration parameters can expose (or exclude) the manifestation of new physics, and shed light on the laws that govern our Universe.Recently, the long-lived but sparse attempts [2] to instrument simulations with optimisation algorithms have grown into a mainstream effort. Current trends in Intelligent-Simulation orchestration stress the need to instruct the computational surveys to learn from previous runs, but they do not address the question of which information it would be most valuable to extract. A theoretical formalism to classify the information processed by large computational models is simply absent. The main objective of this project is to develop a roadmap for the definition of such a formalism.The key question is how one can optimally learn from large computational models. This is a deep, overarching issue affecting experimental as well as computational science, and has been recently proven to be an NP hard problem [3]. Correspondingly, the common approach to simulation data reduction is often pragmatic rather than formal: if solutions with specific properties (such as a certain aerodynamic drag coefficient) are sought, those properties are directly turned into objective functions, taking the control variables as input arguments. This is reasonable when these properties depend only mildly on the input; in the case of anomalous solutions, however, this is often not the case, so one wonders whether more powerful predictors of a simulation's behaviour could be extracted from other, apparently unrelated information contained in the digital model. If so, exposing this information to the machine-learning algorithms could arguably lead to more efficient and exhaustive searches. The investigation of this possibility is the core task that this project aims to undertake.

构建复杂物理现象的高保真数字模型，以及更重要的是将其部署为科学和工程的研究工具，是当今科学计算的一些最关键的事业。没有计算模型，对空间不规则、多尺度或高度耦合的非线性物理系统的研究将是难以处理的。然而，即使有计算模型可用，为了优化探索和发现，调整它们的物理和几何参数(有时被称为控制变量)也是一项巨大的努力。除了大规模并行计算固有的技术挑战之外，这项任务还因为大规模系统的科学复杂性而变得复杂，在大规模系统中，许多自由度可以组合在一起，生成新出现的、异常的、共振的特征，这些特征随着模型保真度的提高而变得越来越明显(例如，在动荡的场景中)。这些特征可能对应于非常有趣的系统配置，但它们通常太过短暂或孤立在控制空间中，无法仅使用蛮力计算来发现。然而，今天的大多数计算调查都是由随机的猜测(尽管有些受到本能的教育)引导的。错过现象学的可能性简直是无法量化的。在许多领域，异常解决方案可以描述极端天气等危及生命的事件。在特定条件下，工业系统的数字模型可能揭示出对周围环境的异常响应，这可能导致效率降低、材料疲劳和结构失效。正是由于它们的独特性和灾难性，以及罕见和短暂的寿命，这些构型也是最难预测的。因此，我们定位异常动态可能发生的位置的能力的任何改进，都可能极大地影响我们防范极端事件的能力。更根本的是，确定在计算模型中实现的一组方程是否能够针对特定的配置参数重现特定的、奇异的解(例如罕见的天文瞬变[1])，这可以揭示(或排除)新物理的表现，并阐明支配我们宇宙的定律。最近，用优化算法测量模拟的长期但稀少的尝试已经成为一种主流努力。智能模拟协调的当前趋势强调需要指示计算调查从以前的运行中学习，但它们没有解决提取哪些信息最有价值的问题。对由大型计算模型处理的信息进行分类的理论形式主义是完全缺乏的。这个项目的主要目标是为这种形式主义的定义制定一个路线图。关键问题是如何从大型计算模型中进行最佳学习。这是一个影响实验和计算科学的深层次问题，最近已被证明是一个NP难题[3]。相应地，常用的模拟数据简化方法往往是实用的，而不是形式的：如果要寻求具有特定属性(如某一气动阻力系数)的解，则将这些属性直接转化为目标函数，将控制变量作为输入变量。当这些属性仅轻微依赖于输入时，这是合理的；然而，在异常解决方案的情况下，情况往往并非如此，因此人们想知道，是否可以从数字模型中包含的其他表面上无关的信息中提取对模拟行为的更强大的预测因子。如果是这样的话，将这些信息暴露给机器学习算法可能会带来更高效、更彻底的搜索。对这种可能性的调查是本项目旨在承担的核心任务。