ADARNet: Deep Learning Predicts Adaptive Mesh Refinement

Deep Learning (DL) algorithms have gained popularity for super-resolution tasks - reconstructing a high-resolution (HR) output from its low-resolution (LR) counterpart. However, current DL approaches, both in computer vision and computational fluid dynamics (CFD), perform spatially uniform super-resolution. Therefore, DL for CFD approaches often over-resolve regions of the LR input that are already accurate at low numerical precision. This hardware over-utilization limits their scalability. To address this limitation, we propose ADARNet, a DL-based adaptive mesh refinement (AMR) framework. ADARNet takes a LR image as input and outputs its non-uniform HR counterpart, predicting HR only in areas that require higher numerical accuracy. As a result, ADARNet predicts the target 1024 x 1024 solution 7- 28.5x faster than state-of-the-art DL methods and reduces the memory usage by 4.4 - 7.65 x while maintaining the same level of accuracy. Moreover, unlike traditional AMR solvers that refine the mesh iteratively, ADARNet is a one-shot method that accelerates it by 2.6 - 4.5x.