Deep Capsule Encoder-Decoder Network for Surrogate Modeling and Uncertainty Quantification
This addresses uncertainty quantification in mechanics, such as heat conduction and diffusion processes, but is incremental as it adapts existing capsule networks to a new application.
The paper tackled surrogate modeling and uncertainty quantification for mechanical systems from sparse data by proposing a capsule-based deep encoder-decoder network, achieving accurate, efficient, and robust results for an elliptic stochastic partial differential equation with input dimensionality of 1024.
We propose a novel \textit{capsule} based deep encoder-decoder model for surrogate modeling and uncertainty quantification of systems in mechanics from sparse data. The proposed framework is developed by adapting Capsule Network (CapsNet) architecture into image-to-image regression encoder-decoder network. Specifically, the aim is to exploit the benefits of CapsNet over convolution neural network (CNN) $-$ retaining pose and position information related to an entity to name a few. The performance of proposed approach is illustrated by solving an elliptic stochastic partial differential equation (SPDE), which also governs systems in mechanics such as steady heat conduction, ground water flow or other diffusion processes, based uncertainty quantification problem with an input dimensionality of $1024$. However, the problem definition does not the restrict the random diffusion field to a particular covariance structure, and the more strenuous task of response prediction for an arbitrary diffusion field is solved. The obtained results from performance evaluation indicate that the proposed approach is accurate, efficient, and robust.