论文标题
组合数据和深度学习模型不确定性：应用固体燃料回归速率的应用
Combined Data and Deep Learning Model Uncertainties: An Application to the Measurement of Solid Fuel Regression Rate
论文作者
论文摘要
在复杂的物理过程中，例如对固体混合火箭燃料的回归速率的测量，其中观察数据和所使用的模型均具有不确定性的源自多个来源，以系统的方式将这些燃料（QOI）组合在一起（QOI）仍然是一个挑战。在本文中，我们提出了一个正向传播不确定性量化（UQ）过程，以生成观察到的回归率$ \ dot {r} $的概率分布。我们从实验中表征了两个输入数据不确定性源（从相机$ u_c $和非零角燃料放置$u_γ$），深度神经网络（$ u_m $）（$ u_m $）（$ u_m $）的不确定性以及用于培训IT的手动分割图像（$ u_s $）的变异性的预测和模型。我们就这些不确定性来源与模型形成不确定性的组合进行了七个案例研究。本文的主要贡献是涉及实验图像数据不确定性的研究和包含，以及当QOI是多个顺序过程的结果时，如何将它们包括在工作流程中。
In complex physical process characterization, such as the measurement of the regression rate for solid hybrid rocket fuels, where both the observation data and the model used have uncertainties originating from multiple sources, combining these in a systematic way for quantities of interest(QoI) remains a challenge. In this paper, we present a forward propagation uncertainty quantification (UQ) process to produce a probabilistic distribution for the observed regression rate $\dot{r}$. We characterized two input data uncertainty sources from the experiment (the distortion from the camera $U_c$ and the non-zero angle fuel placement $U_γ$), the prediction and model form uncertainty from the deep neural network ($U_m$), as well as the variability from the manually segmented images used for training it ($U_s$). We conducted seven case studies on combinations of these uncertainty sources with the model form uncertainty. The main contribution of this paper is the investigation and inclusion of the experimental image data uncertainties involved, and how to include them in a workflow when the QoI is the result of multiple sequential processes.
