We propose structure-preserving neural-network-based numerical schemes to solve both $L^2$-gradient flows and generalized diffusions. In more detail, by using neural networks as tools for spatial discretizations, we introduce a structure-preserving Eulerian algorithm to solve $L^2$-gradient flows and a structure-preserving Lagrangian algorithm to solve generalized diffusions. The Lagrangian algorithm for the generalized diffusion evolves the "flow map" which determines the dynamics of the generalized diffusion. This avoids computing the Wasserstein distance between two probability functions, which is non-trivial. The key ideas behind these schemes are to construct numerical discretizations based on the variational formulations of the gradient flows, i.e., the energy-dissipation laws, directly. More precisely, we construct minimizing movement schemes for these two types of gradient flow by introducing temporal discretization first, which is more efficient and convenient in neural-network-based implementations. The variational discretizations ensure the proper energy dissipation in numerical solutions and are crucial for the long-term stability of numerical computation. The neural-network-based spatial discretization enables us to solve these gradient flows in high dimensions. Various numerical experiments are presented to demonstrate the accuracy and energy stability of the proposed numerical approaches.
翻译:我们提出基于结构保存神经网络的数字方案,以解决以L $2美元为梯度的流动和普遍扩散。更详细地说,通过使用神经网络作为空间离散的工具,我们引入了一种结构保存 Eulelian 算法,以解决以L $2美元为梯度的流动和结构保存 Lagrangian 算法,以解决普遍扩散。拉格朗格用于普及传播的算法发展了“流图”,该算法决定了普遍扩散的动态。这避免了计算瓦瑟斯坦两个概率函数之间的距离,这两个函数是非三角的。这些计划背后的关键想法是,根据梯度流动的变换配方构建数字离异化,即直接构建能量分散法。更准确地说,我们为这两种类型的梯度流动构建了最小化的移动计划,首先引入时间离散化,这在基于神经网络的实施中更加高效和方便。 变异离分化确保数字解决方案中适当的能源分解,对于长期的离性解决方案至关重要。这些组合背后的关键是基于梯度流的数值,即能量偏差度变化定度法。我们所展示的数值稳定度的数值计算方法,使这些数值的数值稳定度的数值流成为了我们提出的数字级。