论文标题
有限观察结果的流量重建和不确定性定量的半条件变异自动编码器
Semi Conditional Variational Auto-Encoder for Flow Reconstruction and Uncertainty Quantification from Limited Observations
论文作者
论文摘要
我们提出了一个新的数据驱动模型，以从空间稀疏的观测值中重建非线性流。该模型是条件变分自动编码器（CVAE）的一个版本，该版本允许进行概率重建，从而对预测进行不确定性量化。我们表明，在我们的模型中，根据完整流量数据的测量结果的条件导致CVAE只有解码器取决于测量值。因此，我们将模型称为半条件变异自动编码器（SCVAE）。通过围绕玻璃海洋模型的圆柱和底部电流的2D流量的模拟，在速度数据上说明了该方法，重建和相关的不确定性估计值。将重建误差与Gappy正确的正交分解（GPOD）方法进行比较。
We present a new data-driven model to reconstruct nonlinear flow from spatially sparse observations. The model is a version of a conditional variational auto-encoder (CVAE), which allows for probabilistic reconstruction and thus uncertainty quantification of the prediction. We show that in our model, conditioning on the measurements from the complete flow data leads to a CVAE where only the decoder depends on the measurements. For this reason we call the model as Semi-Conditional Variational Autoencoder (SCVAE). The method, reconstructions and associated uncertainty estimates are illustrated on the velocity data from simulations of 2D flow around a cylinder and bottom currents from the Bergen Ocean Model. The reconstruction errors are compared to those of the Gappy Proper Orthogonal Decomposition (GPOD) method.
