Inverse modeling of biochemical transport

这一建议解决了目前对适用于统计信号处理的弥散过程的计算效率的逆模型的需求。前向扩散模型在过去一直是很有研究兴趣的主题，因为它们能够充分地数学表示各种应用中的各种过程：环境监测、国土安全、药物输送、股市价格波动、大气状况监测等。对这种方法的补充是反向建模，在该方法中，使用真实的数据测量来估计一组未知参数。也就是说，在现实中，影响扩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(如湍流)的依赖，反演模型往往需要大量的近似，这反过来又影响我们准确估计未知参数和/或预测未来弥散过程的能力。

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.