In this work we set the stage for a new probabilistic pathwise approach to effectively calibrate a general class of stochastic nonlinear fluid dynamics models. We focus on a 2D Euler SALT equation, showing that the driving stochastic parameter can be calibrated in an optimal way to match a set of given data. Moreover, we show that this model is robust with respect to the stochastic parameters.
翻译:在这项工作中,我们为一种新的概率方法奠定了基础,以有效校准一般类的随机非线性流体动态模型。我们侧重于 2D Euler SALT 等式,这表明驾驶的随机参数可以优化校准,以匹配一组给定数据。此外,我们还表明,这一模型在随机参数方面是稳健的。