ITR: A Prototype to Enable Near Real-time Environmental Characterization on the Grid

0312841 Mahinthakumar环境表征在环境事件的实时预测和设计有效的环境保护，清理，风险评估和减灾战略中变得越来越重要。 环境表征的一个基本组成部分是从稀疏测量中推导信息的过程（例如，来自现场监测器或卫星照片的数据）。 例如，在根据从少数监测威尔斯井收集的数据确定地下水污染的特征时，理想的结果可能包括地下污染物浓度的三维地图和污染物速度场的估计。 如果地下特征或污染源的特性不佳，则分析还可以识别孔隙度场或污染源的数量、位置和强度。 未知系统特性（例如，污染源的位置和强度）从观测数据中求解被称为环境逆问题。 在这些问题中，污染物的传输和化学是由一个数学模型组成的偏微分方程（PDE）的系统。 解决未知问题特性是一个优化问题，需要对偏微分方程进行成百上千次的求解。 基于递归的搜索技术代表了求解反问题的最新技术。 另一种方法是使用全局优化算法，如遗传算法，以提供决策空间的更鲁棒的搜索。 一旦全局搜索已经识别出解空间的良好区域，就可以使用梯度或非梯度局部搜索技术进行微调。 网格计算的出现（例如，NSF的新Tera网格）和相关的可用计算能力的增加产生了新的可能性。 由于异构资源的复杂结构，将计算有效地映射到网格资源对于实际应用来说极具挑战性。这个建议的总体目标是调查正式的计算方法，可以很容易地利用网格计算的环境表征问题的解决方案。为此，我们将开发一个支持网格的软件框架。 两个迭代的范例，基于网格启用版本的MPI和Java，分别，将探讨。该框架将适用于地下水和空气污染问题，这两个问题都具有重大的社会意义。 承认即使在网格环境中，逆问题在计算上也是具有挑战性的，模型代理（例如，使用人工神经网络的统计近似值）。 此外，建模生成的替代品（MGA），一种技术，确定了一组良好的，但非常不同的解决方案，将研究通过人机交互动态引导搜索过程。通过MGA生成的替代方案也将用于解决常见的非唯一性问题（即，其中多个特征可以匹配相同的测量数据）。 更广泛的影响：将研究的几个核心组成部分整合到高性能计算和工程系统分析的现有研究生和本科生课程中，将促进发现和理解，同时促进教学和学习。

0312841 Mahinthakumar Environmental characterization is becoming increasingly important in real-time forecasting of environmental events and in designing effective strategies for environmental protection, cleanup, risk assessment, and disaster mitigation. An essential component of environmental characterization is the process of deducing information from sparse measurements (e.g., data from in-situ monitors or satellite photos). For example, in characterization of groundwater pollution from data collected from a small number of monitoring wells, desirable outcomes may include a three-dimensional map of underground pollutant concentrations and an estimate of the pollutant velocity field. If the subsurface features or the source of contamination are poorly characterized, the analysis may also identify the porosity field or the number, location, and strength of pollutant sources. Problems in which unknown system characteristics (e.g., pollutant source location and strength) are resolved from observed data are known as environmental inverse problems. In these problems, pollutant transport and chemistry are represented by a mathematical model consisting of a system of partial differential equations (PDEs). Resolving the unknown problem characteristics is an optimization problem that requires the PDEs to be solved hundreds-to-thousands of times. Gradient-based search techniques represent the state-of-the-art for solving inverse problems. An alternative is to use global optimization heuristics, such as genetic algorithms, to provide a more robust search of the decision space. Once global search has identified good regions of the solution space, gradient or non-gradient local search techniques may be used for fine-tuning. The emergence of grid computing (e.g., NSF's new Tera Grid) and the associated increase in available computing power yield new possibilities. Efficient mapping of computations to grid resources has proven extremely challenging for realistic applications due to the complex fabric of heterogeneous resources. The overall goal of this proposal is to investigate formal computational approaches that can readily harness grid computing for the solution of environmental characterization problems. To this end, we will develop a grid-enabled software framework. Two iternative paradigms, based on the grid-enabled version of MPI and Java, espectively, will be explored. The framework will be applied to groundwater and air pollution problems, both of which are of prime societal importance. Acknowledging that, even within a grid environment, inverse problems are computationally challenging, model surrogates (e.g., statistical approximations using artificial neural networks) will be explored. Further, Modeling to Generate Alternatives (MGA), a technique that identifies a set of good yet very different solutions, will be investigated for dynamically steering the search process through human-computer interaction. Alternatives generated via MGA will be used also to address the commonly encountered non-uniqueness issue (i.e., where multiple characterizations can match the same measured data) in inverse problems. Broader Impact: Integration of several core components of research into existing graduate and undergraduate courses in high performance computing and engineering systems analysis will advance discovery and understanding while promoting teaching and learning.