Computational framework for fast uncertainty quantification and decision analytics

用于快速不确定性量化和决策分析的计算框架

• 批准号: 557220-2020
• 负责人: Nair, PrasanthBalagopal
• 金额: $ 9.11万
• 依托单位: University of Toronto
• 依托单位国家: 加拿大
• 项目类别: Idea to Innovation
• 财政年份: 2020
• 资助国家: 加拿大
• 起止时间: 2020-01-01 至 2021-12-31
• 项目状态: 已结题
• 来源: https://www.nserc-crsng.gc.ca/ase-oro/Details-Detailles_eng.asp?id=706604
• 关键词: Computational; framework; fast; uncertainty; quantification

We have developed a novel deep Bayesian learning architecture coupled to a Bayesian analytics engine which together form a powerful and highly flexible computational framework for decision support in complex engineering applications. We call our system the Deep Bayesian Analytics Engine (DBAE). At the fundamental level, our invention allows the discovery of actionable relationships between decision variables and performance outcomes by overcoming the curse of dimensionality in challenging, high-dimensional decision making problems. A distinguishing feature of our technology is its ability to infer the intrinsic/effective dimensionality of the problem via a statistically rigorous nonlinear decomposition method that facilitates the exploitation of the underlying problem structure for accelerated analytics computations. Specifically, our framework enables dramatic speedups in tasks that routinely arise in the computation of decision analytics such as the evaluation of high-dimensional integrals to estimate predictive statistics, search/optimization in high-dimensional spaces, decision robustness calculations, statistical calibration of computer models using data from physical experiments and onboard sensors, and the solution of inverse problems. We have evaluated a prototype software tool on challenging benchmarks and industrial-scale problems from the aircraft engine design sector. In the proposed project, we will enhance our prototype software implementation in order to begin field testing with potential partners and end users. Work will focus on scaling and accelerating our core algorithms in conjunction with the development of an improved user interface with immersive visualization capabilities. To broaden our potential market, additional testing will be carried out on novel applications in the environmental sciences and online system health monitoring sectors where there is significant demand for such advanced statistical tools.

我们开发了一种新的深度贝叶斯学习体系结构，与贝叶斯分析引擎相结合，共同形成了一个强大且高度灵活的计算框架，用于复杂工程应用中的决策支持。我们称我们的系统为深度贝叶斯分析引擎(DBAE)。在基本层面上，我们的发明通过在具有挑战性的高维决策问题中克服维度诅咒，允许发现决策变量和绩效结果之间的可操作关系。我们技术的一个显著特点是，它能够通过统计上严格的非线性分解方法来推断问题的内在/有效维度，该方法有助于开发用于加速分析计算的潜在问题结构。具体地说，我们的框架能够显著加速决策分析计算中经常出现的任务，例如评估高维积分以估计预测统计、在高维空间中搜索/优化、决策稳健性计算、使用物理实验和机载传感器的数据对计算机模型进行统计校准，以及逆问题的解决。 我们已经评估了一个原型软件工具，用于挑战基准和来自飞机发动机设计部门的工业规模问题。在拟议的项目中，我们将加强我们的原型软件实施，以便开始与潜在的合作伙伴和最终用户进行实地测试。我们的工作重点将是扩展和加速我们的核心算法，同时开发具有沉浸式可视化能力的改进用户界面。为了扩大我们的潜在市场，将对环境科学和在线系统健康监测部门的新应用进行额外的测试，这些部门对这种先进的统计工具有很大的需求。