| Abstract: | Algorithms based on deep neural networks (DNNs) have attracted increasing attention from the scientific computing community. DNN based algorithms are
easy to implement, natural for nonlinear problems, and have shown great potential to
overcome the curse of dimensionality. In this work, we utilize the multi-scale DNN-based algorithm (MscaleDNN) proposed by Liu, Cai and Xu (2020) to solve multi-scale
elliptic problems with possible nonlinearity, for example, the p-Laplacian problem.
We improve the MscaleDNN algorithm by a smooth and localized activation function.
Several numerical examples of multi-scale elliptic problems with separable or non-separable scales in low-dimensional and high-dimensional Euclidean spaces are used
to demonstrate the effectiveness and accuracy of the MscaleDNN numerical scheme. |
