The Regularised Inertial Dean-Kawasaki model (RIDK) is a nonlinear stochastic PDE which captures fluctuations around the mean-field limit for large-scale particle systems in both density and momentum. We focus on the following two aspects. Firstly, we set up a discontinuous Galerkin (DG) discretisation scheme for the RIDK model: we provide suitable definitions of numerical fluxes at the interface of the mesh elements which are consistent with the wave-type nature of the RIDK model and grant stability of the simulations, and we quantify the rate of convergence in mean square to the continuous RIDK model. Secondly, we introduce modifications of the RIDK model in order to preserve positivity of the density (such a feature does not hold for the original RIDK model). By means of numerical simulations, we show that the modifications lead to physically realistic and positive density profiles. In one case, subject to additional regularity constraints, we also prove positivity. Finally, we present an application of our methodology to a system of diffusing and reacting particles. Our Python code is available in open-source format.
翻译:固定化的 Dean- Kawasaki 型号(RIDK) 是一个非线性随机式PDE 模型,它捕捉大型粒子系统在密度和动力两方面的平均场限周围的波动。 我们集中关注以下两个方面。 首先,我们为RIDK 型号设置了一个不连续的 Galerkin (DG) 离散方案: 我们提供了与 RIDK 型号的波型特性相一致的网目元素界面上的数字通量的适当定义, 并赋予模拟的稳定性, 我们用正方平均值来量化与连续的 RIDK 型号的趋同率。 其次, 我们对RIDK 型号模型进行了修改, 以保持密度的假设性( 这种特性对原RIDK 型号没有保留特性 ) 。 我们通过数字模拟, 显示这些修改导致物理上现实和积极的密度剖面。 在一种情况下, 受其他常规性制约, 我们还证明了真实性。 最后, 我们展示了我们的方法应用于调控和反应粒子系统的应用。 我们的Python 代码是开放源格式。