Assessments of epistemic uncertainty using Gaussian stochastic weight averaging for fluid-flow regression

TL;DR

This paper demonstrates that Gaussian stochastic weight averaging (SWAG) effectively quantifies epistemic uncertainty in neural network models applied to fluid flow problems, providing interpretable confidence intervals across diverse datasets.

Contribution

The study introduces SWAG for uncertainty quantification in fluid-flow neural network models, showing its applicability across various architectures and complex datasets.

Findings

SWAG provides physically-interpretable confidence intervals.

The method is applicable to different neural network architectures.

SWAG performs well on diverse fluid flow datasets.

Abstract

We use Gaussian stochastic weight averaging (SWAG) to assess the model-form uncertainty associated with neural-network-based function approximation relevant to fluid flows. SWAG approximates a posterior Gaussian distribution of each weight, given training data, and a constant learning rate. Having access to this distribution, it is able to create multiple models with various combinations of sampled weights, which can be used to obtain ensemble predictions. The average of such an ensemble can be regarded as the `mean estimation', whereas its standard deviation can be used to construct `confidence intervals', which enable us to perform uncertainty quantification (UQ) with regard to the training process of neural networks. We utilize representative neural-network-based function approximation tasks for the following cases: (i) a two-dimensional circular-cylinder wake; (ii) the DayMET dataset (maximum daily temperature in North America); (iii) a three-dimensional square-cylinder wake; and (iv) urban flow, to assess the generalizability of the present idea for a wide range of complex datasets. SWAG-based UQ can be applied regardless of the network architecture, and therefore, we demonstrate the applicability of the method for two types of neural networks: (i) global field reconstruction from sparse sensors by combining convolutional neural network (CNN) and multi-layer perceptron (MLP); and (ii) far-field state estimation from sectional data with two-dimensional CNN. We find that SWAG can obtain physically-interpretable confidence-interval estimates from the perspective of model-form uncertainty. This capability supports its use for a wide range of problems in science and engineering.