The reconstruction method is based on K-localized spectral graph filters, wherewith graph convolution on water networks is possible.
The main contribution of the presented approach is that the agent can run the pumps in real-time because it depends only on measurement data.
This database was used to train an encoder-decoder style deep convolutional neural network to predict the velocity distribution from the geometry.
In our approach, the agent is trained in a simulated environment and it is able to navigate both in a simulated and real-world environment.
The transformer network architecture is completely based on attention mechanisms, and it outperforms sequence-to-sequence models in neural machine translation without recurrent and convolutional layers.
The aim of this paper is to introduce two widely applicable regularization methods based on the direct modification of weight matrices.
Given two distinct datasets, an important question is if they have arisen from the the same data generating function or alternatively how their data generating functions diverge from one another.