Intrusive deconvolutional neural networks for enhancing PIC/FLIP solutions

Abstract

Traditional fluid flow predictions require large computational resources. Despite recent progress in parallel and GPU computing, the ability to run fluid flow predictions in real-time is often infeasible. Recently developed machine learning approaches, which are trained on high-fidelity data, perform unsatisfactorily outside the training set and remove the ability of utilising legacy codes after training. We propose a novel methodology that uses a deep learning approach that can be used within a low-fidelity fluid flow solver to significantly increase the accuracy of the low-fidelity simulations. The resulting solver enables accurate while reducing computational times up to 100 times. The deep neural network is trained on a combination of low- and high-fidelity data, and the resulting solver is referred to as a multi-fidelity solver. The proposed methodology is demonstrated by means of enhancing a fluid flow simulator, known as PIC/FLIP, which is a popular fluid flow simulator in the field of computer generated imagery.

References in corpus (4)

• Unsupervised Representation Learning with Deep Convolutional Generative Adversarial Networks
• Fast and Accurate Deep Network Learning by Exponential Linear Units (ELUs)
• NSFnets (Navier-Stokes Flow nets): Physics-informed neural networks for the incompressible Navier-Stokes equations
• A guide to convolution arithmetic for deep learning