5 citations · 11 across the 7 of their papers we have counts for
13 papers
GCN-FFNN: A Two-Stream Deep Model for Learning Solution to Partial Differential Equations
Onur Bilgin, Thomas Vergutz, Siamak Mehrkanoon
This paper introduces a novel two-stream deep model based on graph convolutional network (GCN) architecture and feed-forward neural networks (FFNN) for learning the solution of non…
AA-TransUNet: Attention Augmented TransUNet For Nowcasting Tasks
Yimin Yang, Siamak Mehrkanoon
Data driven modeling based approaches have recently gained a lot of attention in many challenging meteorological applications including weather element forecasting. This paper intr…
Multistream Graph Attention Networks for Wind Speed Forecasting
Dogan Aykas, Siamak Mehrkanoon
Reliable and accurate wind speed prediction has significant impact in many industrial sectors such as economic, business and management among others. This paper presents a new mode…
Symbolic regression for scientific discovery: an application to wind speed forecasting
Ismail Alaoui Abdellaoui, Siamak Mehrkanoon
Symbolic regression corresponds to an ensemble of techniques that allow to uncover an analytical equation from data. Through a closed form formula, these techniques provide great a…
Broad-UNet: Multi-scale feature learning for nowcasting tasks
Jesus Garcia Fernandez, Siamak Mehrkanoon
Weather nowcasting consists of predicting meteorological components in the short term at high spatial resolutions. Due to its influence in many human activities, accurate nowcastin…
Deep Graph Convolutional Networks for Wind Speed Prediction
Tomasz Stańczyk, Siamak Mehrkanoon
Wind speed prediction and forecasting is important for various business and management sectors. In this paper, we introduce new models for wind speed prediction based on graph conv…