Data-Driven Self-Supervised Learning for the Discovery of Solution Singularity for Partial Differential Equations
This work addresses the challenge of unknown singularity locations in PDEs for scientific computing, offering a data-driven approach to improve adaptive methods and reduce computational costs, though it appears incremental in applying SSL to this specific domain.
The paper tackles the problem of detecting solution singularities in partial differential equations using only raw data, proposing a self-supervised learning framework with filtering methods to estimate singularity locations, and demonstrates its effectiveness through numerical examples and experiments.
The appearance of singularities in the function of interest constitutes a fundamental challenge in scientific computing. It can significantly undermine the effectiveness of numerical schemes for function approximation, numerical integration, and the solution of partial differential equations (PDEs), etc. The problem becomes more sophisticated if the location of the singularity is unknown, which is often encountered in solving PDEs. Detecting the singularity is therefore critical for developing efficient adaptive methods to reduce computational costs in various applications. In this paper, we consider singularity detection in a purely data-driven setting. Namely, the input only contains given data, such as the vertex set from a mesh. To overcome the limitation of the raw unlabeled data, we propose a self-supervised learning (SSL) framework for estimating the location of the singularity. A key component is a filtering procedure as the pretext task in SSL, where two filtering methods are presented, based on $k$ nearest neighbors and kernel density estimation, respectively. We provide numerical examples to illustrate the potential pathological or inaccurate results due to the use of raw data without filtering. Various experiments are presented to demonstrate the ability of the proposed approach to deal with input perturbation, label corruption, and different kinds of singularities such interior circle, boundary layer, concentric semicircles, etc.