This page contains some conference presentations and posters.
| Neural Networks and Deep Learning have started only recently to become standard tools in simulation and computational sciences, and they have already enabled significant advances, becoming a viable option for the data-driven solution of possibly high-dimensional and parametric PDEs. In the deep learning literature, recent years have seen a growing interest for the development of Geometric Deep Learning (GDL) and Graph Neural Networks (GNNs), which are deep learning techniques applicable to graph[...]. |
With Antonio Longa
| The polynomial kernels are widely used in machine learning and they are one of the default choices to develop kernel-based classification and regression models. However, they are rarely used and considered in numerical analysis due to their lack of strict positive definiteness. In particular they do not enjoy the usual property of unisolvency for arbitrary point sets, which is one of the key properties used to build kernel-based interpolation methods. This work is devoted to establish some initi[...]. |