标题： 贪婪递归参数选择用于单向纳维-斯托克斯（OWNS）方程 显示英文标题

标题： Greedy recursion parameter selection for the One-Way Navier-Stokes (OWNS) equations

摘要： 一维Navier-Stokes（OWNS）方程利用递归滤波来构建线性双曲系统的高效且适定的一维近似。 递归参数对于方法的准确性和稳定性至关重要，并且以前的选择基于关键特征值（或其分支）的经验估计，这种方法收敛缓慢，并且需要针对新系统进行反复调整。 我们回顾了非齐次方程的投影和递归OWNS公式（OWNS-P 和 OWNS-R），并提出了一种用于参数选择的贪婪算法。 我们证明它收敛更快，并导致计算成本的净减少。 显示英文摘要

摘要： The One-Way Navier-Stokes (OWNS) equations use recursive filtering to construct efficient, well-posed one-way approximations to linear hyperbolic systems. The recursion parameters are critical to the accuracy and stability of the method, and have previously been chosen based on heuristic estimates of key eigenvalues (or their branches), which converges slowly and requires trial-and-error tuning for new systems. We review the projection and recursive OWNS formulations (OWNS-P and OWNS-R) for inhomogeneous equations and propose a greedy algorithm for parameter selection. We show that it converges faster and leads to a net decrease in computational cost.