Inverse modeling of biochemical transport

该建议解决了目前需要的计算效率的逆模型的分散过程，服从统计信号处理。在过去，前向色散模型一直是相当大的研究兴趣的主题，因为它们是在各种应用中的不同过程的适当的数学表示：环境监测，国土安全，药物输送，股票市场价格波动，大气状况监测等。这种方法的补充是逆建模，其中一组未知参数估计使用真实的数据测量。即实际上影响色散的一些参数是未知的（例如，污染物泄漏的位置、特定药物的扩散系数等）由于上述过程的复杂性及其对各种复杂参数和现象（例如湍流）的依赖性，逆模型通常需要显著的近似，这反过来又影响我们准确估计未知参数和/或预测未来分散过程的能力。

This proposal addresses current need for computationally efficient inverse models of dispersion processes that are amenable to statistical signal processing. The forward dispersion models have been subject of considerable research interest in the past as they are adequate mathematical representation of diverse processes in various applications: environmental monitoring, homeland security, drug delivery, stock market price fluctuations, atmospheric condition monitoring, etc. Complementary to this approach is inverse modeling in which a set of unknown parameters is estimated using real data measurements. Namely in reality some of the parameters that affect dispersion are unknown (e.g., location of the pollutant leakage, dispersion coefficient for a particular drug, etc.) Due to the complexity of the aforementioned processes and their dependence on a variety of complex parameters and phenomena (e.g. turbulence) inverse models often require significant approximations which in turn affect our ability to accurately estimate unknown parameters and/or predict dispersion process in future.