AI 中文总结
研究受瞬时湍动能模型启发的随机微分方程中强迫参数估计问题，基于期望最大化框架提出估计算法，证明其一致性并通过数值实验说明发现。
AI 中文摘要
本文研究了一个受描述瞬时湍动能模型启发的随机微分方程中强迫参数的估计问题。所分析的随机微分方程是非线性McKean-Vlasov型的，其漂移项依赖于解的期望值的幂次，这在代数意义上也引入了非线性。我们提出了一种基于期望最大化框架的估计算法，并证明了该方法的一致性。通过数值实验说明了我们的发现。
英文摘要
In this article, we address the problem of estimating a forcing parameter in a stochastic differential equation inspired by a model that describes instantaneous turbulent kinetic energy. The stochastic differential equation we analyze is of the nonlinear McKean-Vlasov type, where the drift term depends on a power of the expected value of the solution, which also introduces nonlinearity in an algebraic sense. We propose an estimation algorithm based on the Expectation-Maximization framework and show the consistency of our method. We illustrate our findings through numerical experiments.
Comments24 pages, 3 figures
