论文标题
用于替代建模和不确定性定量的深胶囊编码器网络
Deep Capsule Encoder-Decoder Network for Surrogate Modeling and Uncertainty Quantification
论文作者
论文摘要
我们提出了一种基于新颖的\ textit {capsule}，用于从稀疏数据中对机械中系统中系统的替代建模和不确定性量化的深度编码模型。提出的框架是通过将胶囊网络（CAPSNET）体系结构改编为图像到图像回归编码器网络的。具体而言，目的是利用Capsnet对卷积神经网络（CNN）$ - $保留姿势和与实体有关的位置信息的好处。通过求解椭圆形的随机部分微分方程（SPDE）来说明拟议方法的性能，该方程还控制着诸如稳定的热传导，地下水流量或其他扩散过程等力学中的系统，基于不确定性量化问题，输入尺寸为1024美元。但是，问题定义并未将随机扩散字段限制为特定的协方差结构，并且解决了任意扩散场的响应预测更为艰巨的任务。从性能评估获得的结果表明，所提出的方法是准确，有效且健壮的。
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.
