ADARNet: Deep Learning Predicts Adaptive Mesh Refinement

Octavi Obiols-Sales, Abhinav Vishnu, Nicholas Malaya, Aparna Chandramowlishwaran

Abstract: 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 × 1024 solution 7 − 28.5 × faster than state-of-the-art DL methods and reduces the memory usage by 4.4 − 7.65 × 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 − 4.5 ×.