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

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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.