Speaker
Medard Govoeyi
Description
Training a neural network to learn differential equations requires a significant amount of data. Collecting the data set is sometimes difficult or expensive. When we have a limited budget, it is important to optimize the process of selecting the data we should use to train the neural network. We present two methods for selecting an optimal data set. First, we present a way to select the optimal data set through Optimal Experimental Design(OED) and learn the differential equations with the optimal data set. Second, inspired by the Dual Weighted Residual(DWR) method, we optimize the data selection process for learning differential equations using the Deep Ritz method.
