Inverse modeling of biochemical transport

这一建议解决了当前需要的计算效率的逆模型的分散过程是适用于统计信号处理。前向弥散模型在过去一直受到相当大的研究兴趣，因为它们是各种应用中不同过程的充分数学表示：环境监测，国土安全，药物输送，股票市场价格波动，大气条件监测等。与此方法互补的是逆建模，其中使用实际数据测量估计一组未知参数。也就是说，在现实中，一些影响分散的参数是未知的（例如，污染物泄漏的位置，特定药物的分散系数等）。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，逆模型通常需要显著的近似值，这反过来影响了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.