New Paper out
The paper proposes a new data-driven finite volume model that combines the semi-discrete form of the energy balance with a temporal convolutional neural network.
This work proposes a new data-driven finite volume model that combines the semi-discrete form of the energy balance with a temporal convolutional neural network. Compared to recurrent neural networks, the model is energy-conserving and computes temperature profiles on a grid of positions in parallel, thus executing substantially faster than the actual process.
Full access to the paper can be found external page here.
