Learning effective SDEs from Brownian dynamic simulations of colloidal particles
Learning effective SDEs from Brownian dynamic simulations of colloidal particles
复制标题
从胶体粒子的布朗动态模拟中学习有效的 SDE
DOI：
10.1039/d2me00086e
复制
发表时间：
2023
影响因子：
3.6
通讯作者：
Kevrekidis, Ioannis G.
中科院分区：
文献类型：
--
作者：
Evangelou, Nikolaos;Dietrich, Felix;Bello-Rivas, Juan M.;Yeh, Alex J.;Hendley, Rachel S.;Bevan, Michael A.;Kevrekidis, Ioannis G.
我们使用从布朗动力学模拟获得的数据构建了一个简化的、数据驱动的、参数依赖的有效随机微分方程（eSDE），用于电场介导的胶体结晶。我们使用扩散图（一种流形学习算法）来识别一组有用的潜在可观察量。在这个潜在空间中，我们使用受数值随机积分器启发的深度学习架构来识别 eSDE，并将其与传统的 Kramers-Moyal 展开估计进行比较。我们表明，获得的变量和学习的动力学准确地编码了布朗动力学模拟的物理原理。我们进一步说明，我们的简化模型捕获了相应实验数据的动态。我们的降维/简化模型识别方法可以轻松移植到广泛的粒子系统动力学实验/模型中。
We construct a reduced, data-driven, parameter dependent effective stochastic differential equation (eSDE) for electric-field mediated colloidal crystallization using data obtained from Brownian dynamics simulations. We use diffusion maps (a manifold learning algorithm) to identify a set of useful latent observables. In this latent space we identify an eSDE using a deep learning architecture inspired by numerical stochastic integrators and compare it with the traditional Kramers–Moyal expansion estimation. We show that the obtained variables and the learned dynamics accurately encode the physics of the Brownian dynamic simulations. We further illustrate that our reduced model captures the dynamics of corresponding experimental data. Our dimension reduction/reduced model identification approach can be easily ported to a broad class of particle systems dynamics experiments/models.