ATD: Data-driven stochastic source inversion algorithms for event reconstruction of biothreat agent dispersion

现实的事件重建问题需要基于偏微分方程的模型，而将传感器数据融入其中对于紧急响应目的来说必须是有效的。这位研究人员和她的同事将使用数值线性代数中的技术有效地解决这个问题，这些技术不需要对正向模型进行多次模拟。这些技术可以从贝叶斯的角度来看，分别寻找点估计和不确定性估计的第一时刻和第二时刻。最小二乘估计将用逆协方差矩阵加权，这是作为这项工作的一部分而开发的一种新技术，它不需要正态分布的误差。这些权重使最小二乘估计更准确，而且该方法在计算上比完全贝叶斯方法更有效。这位研究人员和她的同事将量化基于PDE的远期模型中的不确定性，同时考虑数据和参数的不确定性。这些基于PDE的模型将采用多GPU计算范式来全面加速威胁检测算法，预计将产生三维污染物扩散模型的近实时反演。这个问题的动机是这样一个事实，即在2008年6月提交给国会请求者的报告(GAO-08-180)中，政府问责局(GAO)发现？尽管国土安全部(DHS)和其他机构已经采取措施改善国土防御，但地方急救人员仍然没有工具来准确地立即识别什么、何时、何地以及多少化学、生物、辐射、或者核材料在美国城市地区被意外释放或由恐怖分子释放？国土安全部已经在几个主要城市部署了生物观察计划，以监测空气中的生物治疗剂。城市地区的传感器数量有限，不能仅仅通过测量来可靠地说明化学-生物扩散事件及其对人口的影响。PI和她的同事们将开发计算快速的数学算法，以重建由传感器网络检测到的化学或生物制剂的分散情况。这将使急救人员能够确定和量化化学-生物制剂释放的位置和数量。一旦在时间上回溯扩散事件，就可以使用高保真的大气传输和扩散模型来预测危险区域，以便进行应急响应和减灾。正在考虑的问题在战场上的防御行动中也同样重要，对化学-生物毒剂释放的位置、强度和时间的估计可以为战术决策提供支持，如要避免的区域、防护装备的使用和医疗反应。

Realistic event reconstruction problems require PDE-based models, and incorporating sensor data into them must be efficient for emergency response purposes. The investigator and her colleagues will efficiently solve this problem using techniques from numerical linear algebra that do not require multiple simulations of the forward model. These techniques can be viewed from the Bayesian perspective as finding first and second moments for point and uncertainty estimates, respectively. Least squares estimates will be weighted with inverse covariance matrices found by a new technique developed as part of this work that does not require normally distributed errors. These weights make least squares estimates more accurate, and the approach is computationally more efficient than full Bayesian methods. The investigator and her colleagues will quantify uncertainty in the PDE based forward model, while accounting for both data and parameter uncertainty. These PDE-based models will adopt a multi-GPU computing paradigm for overall acceleration of the algorithms for threatdetection, and it is expected that near real-time inversions of the three-dimensional contaminant dispersion model will be produced.This problem is motivated by the fact that in their June 2008 report (GAO-08-180) to Congressional requesters, the Government AccountabilityOffice (GAO) has found that ?While the Department of Homeland Security (DHS) and other agencies have taken steps to improve homeland defense, local first responders still do not have tools to accurately identify right away what, when, where, and how much chemical, biological, radiological, or nuclearmaterials are released in U.S. urban areas, accidentally or by terrorists?. DHS has deployed the BioWatch program in several major cities to monitor the air for biothreat agents. The number of sensors in urban areas is limited, and a reliable account of the chemical-biological dispersion event and its impact on the population cannot be created purely from measurements. The PI and her colleagues will develop computationally fast mathematical algorithms to reconstruct the dispersion of a chemical or biological agent that is detected by a sensor network. This will allow first responders to identify and quantify the location and amount of chemical-biological agent release. Once the dispersion event is backtracked in time it can then be projected forward using high-fidelity atmospheric transport and dispersion models to predict the hazard zone for emergency response and hazard mitigation. The problem under consideration is equally significant in defense operations on the battlefield, where estimates on the location, strength and time of chemical-biological agent release can support tactical decisions such as areas to avoid, protective gear usage and medical response.