An advanced hybrid deep adversarial autoencoder for parameterized nonlinear fluid flow modelling

Abstract

Considering the high computation cost produced in conventional computation fluid dynamic simulations, machine learning methods have been introduced to flow dynamic simulations in recent years. However, most of studies focus mainly on existing fluid fields learning, the prediction of spatio-temporal nonlinear fluid flows in varying parameterized space has been neglected. In this work, we propose a hybrid deep adversarial autoencoder (DAA) to integrate generative adversarial network (GAN) and variational autoencoder (VAE) for predicting parameterized nonlinear fluid flows in spatial and temporal space. High-dimensional inputs are compressed into the low-representation representations by nonlinear functions in a convolutional encoder. In this way, the predictive fluid flows reconstructed in a convolutional decoder contain the dynamic flow physics of high nonlinearity and chaotic nature. In addition, the low-representation representations are applied into the adversarial network for model training and parameter optimization, which enables a fast computation process. The capability of the hybrid DAA is demonstrated by varying inputs on a water collapse example. Numerical results show that this hybrid DAA has successfully captured the spatio-temporal flow features with CPU speed-up of three orders of magnitude. Promising results suggests that the hybrid DAA can play a critical role in efficiently and accurately predicting complex flows in future.

Probabilistic neural networks for fluid flow surrogate modeling and data recovery

“…Another promising avenue for ML is for addressing the challenges of conventional reduced order models (ROMs) [16,17]. Recent literature has demonstrated the capabilities of timeseries methods from ML [18,19,20,21,22,23] for reduced-space temporal dynamics prediction as well as nonlinear subspace identification using image processing techniques [24,25,26,27,28,29]. These methods have demonstrated promising results over conventional equation-based methods such as the proper-orthogonal decomposition (POD) based Galerkin-projection technique [30] which suffers from an inability to handle advection-dominated systems.…”