论文标题
连续数据同化，用于在多孔培养基中的位移
Continuous Data Assimilation for Displacement in a Porous Medium
论文作者
论文摘要
在本文中，我们提出了在多孔介质中使用连续数据同化算法的混溶模型。在没有模型初始条件的情况下，观察到的稀疏测量值用于生成与真实溶液的近似值。在某些假设稀疏测量值及其将其掺入算法的假设下，可以表明，随着时间的推移，所得近似解决方案以指数速率收敛到真实解决方案。为了验证算法的适用性，考虑了各种数值示例。
In this paper we propose the use of a continuous data assimilation algorithm for miscible flow models in a porous medium. In the absence of initial conditions for the model, observed sparse measurements are used to generate an approximation to the true solution. Under certain assumption of the sparse measurements and their incorporation into the algorithm it can be shown that the resulting approximate solution converges to the true solution at an exponential rate as time progresses. Various numerical examples are considered in order to validate the suitability of the algorithm.
