Collaborative Research: DDDAS-TMRP: MIPS: A Real-Time Measurement Inversion Prediction Steering Framework for Hazardous Events

该项目将开发一个多尺度、数据驱动的高性能计算框架，用于根据稀疏测量数据实时重建危险事件，并随后对危险的演变进行概率预测。该框架分为四个阶段，在危险事件的整个生命周期内使用动态获取的数据连续执行。(1)测量：分布式传感器在指定的时间范围内提供动态测量，用于重建事件的初始条件。(2)反演：在稀疏测量的驱动下，求解一个反问题来估计控制危险演化的方程的初始条件。(3)预测：对反演结果的统计分析允许估计初始条件中的不确定性，这些不确定性被传播到对危险及其不确定性的演变的预测中。(4)转向：基于有效性指数将传感器引导到新的位置，该有效性指数结合了关于传感器位置的反演的敏感性、预测中的估计不确定性和人口密度因素。如上所述的测量-反转-预测-转向(MIPS)框架的持续应用导致了对不断演变的危险的最新预测，具有内置的不确定性估计，以及修订的传感器部署策略，改进了预测以减少其不确定性。开发的方法考虑了MIPS框架必须执行的两个决策时间尺度。急救人员需要按秒到分钟进行决策，才能立即开始应对工作。对于这样的时间尺度，偏微分方程(PDE)形式的高保真模型太可怕了。相反，所提出的方法将构建PDE的降阶模型，以便于MIPS框架的实时执行。分钟到小时的决策尺度允许应急官员使用高保真、高分辨率的PDE模型进行更仔细、更有节制的反应。为了能够快速执行这类模型的MIPS周期，该项目将开发快速、可扩展的并行反演和预测算法。为了演示、评估、强化、加固和生成框架，将在特定的应用程序试验台上进行验证：从稀疏测量中预测故意或意外释放的大气污染物的城市/区域扩散。

The project will develop a multiscale, data-driven, high performance computational framework for real-time reconstruction of hazardous events from sparse measurements, and consequent probabilistic prediction of the evolution of the hazard. The framework is distinguished by four phases that are performed continually with dynamically-obtained data over the lifetime of the hazardous event. (1) Measurement: Distributed sensors provide dynamic measurements over a specified time horizon that will be used to reconstruct the initial conditions of the event. (2) Inversion: Driven by the sparse measurements, an inverse problem is solved to estimate the initial conditions for the equations governing the evolution of the hazard. (3) Prediction: Statistical analysis of the inversion results permits estimation of the uncertainty in the initial conditions, which is propagated into a prediction of the evolution of the hazard and its uncertainty. (4) Steering: Sensors are steered to new locations based on an effectivity index that incorporates sensitivities of the inversion with respect to sensor location, estimated uncertainty in the prediction, and population density factors. Continual application of the measure-invert-predict-steer (MIPS) framework described above results in updated predictions of the evolving hazard with built-in uncertainty estimates, as well as revised sensor deployment strategies that refine the predictions to reduce their uncertainty. The methods developed consider two time scales of decision making at which the MIPS framework must execute. The seconds-to-minutes decision-making scale is required by first responders to begin immediate response efforts. For such time scales, high-fidelity models in the form of partial differential equations (PDEs) are too formidable. Instead, the proposed methods will construct reduced-order models of the PDEs to facilitate realtime execution of the MIPS framework. The minutes-to-hours decision-making scale permits more careful and measured response by emergency officials using high-fidelity, high-resolution PDE models. To enable rapid execution of the MIPS cycle for such models, the project will develop fast, scalable, parallel algorithms for inversion and prediction. To demonstrate, assess, harden, robustify, and the resulting framework, will be validated on a specific application testbed: prediction of the urban/regional dispersion of intentionally- or accidentally-released atmospheric contaminants from sparse measurements.