Hybrid deep learning and iterative methods for accelerated solutions of viscous incompressible flow

TL;DR

HyDEA is a hybrid framework combining deep learning and iterative solvers to accelerate and generalize solutions of the pressure Poisson equation in incompressible flow simulations, demonstrating superior efficiency and resolution capabilities.

Contribution

This work introduces HyDEA, a novel hybrid deep learning and iterative method that generalizes across geometries and Reynolds numbers without retraining, significantly improving flow simulation efficiency.

Findings

HyDEA outperforms traditional CG/PCG methods in accuracy and speed.

The framework generalizes across different flow geometries and Reynolds numbers.

HyDEA exhibits super-resolution capabilities, solving finer grids accurately.

Abstract

The pressure Poisson equation, central to the fractional step method in incompressible flow simulations, incurs high computational costs due to the iterative solution of large-scale linear systems. To address this challenge, we introduce HyDEA, a novel framework that synergizes deep learning with classical iterative solvers. It leverages the complementary strengths of a DeepONet - capable of capturing large-scale features of the solution - and the CG or a PCG method, which efficiently resolves fine-scale errors. Specifically, within the framework of line-search methods, the DeepONet predicts search directions to accelerate convergence in solving sparse, symmetric-positive-definite linear systems, while the CG/ PCG method ensures robustness through iterative refinement. The framework seamlessly extends to flows over solid structures via the decoupled immersed boundary projection method. Crucially, the DeepONet is trained on fabricated linear systems rather than flow specific data, endowing it with inherent generalization across geometric complexities and Reynolds numbers without retraining. Benchmarks demonstrate superior efficiency and accuracy of HyDEA over the CG/PCG methods for flows with no obstacles, single or multiple stationary obstacles, and one moving obstacle - using fixed network weights. Remarkably, HyDEA also exhibits super-resolution capability: although the DeepONet is trained on a 128*128 grid for Re=1000, the hybrid solver delivers accurate solutions on a 512*512 grid for Re=10000 via interpolation, despite discretizations mismatch. In contrast, a purely data-driven DeepONet fails for complex flows, underscoring the necessity of hybridizing deep learning with iterative methods. Robustness, efficiency, and generalization across geometries, resolutions, and Reynolds numbers of HyDEA highlight its potential as a transformative solver for real world fluid dynamics problems.