In this paper, we propose a method for bracing direction optimization of grid shells using a Deep Deterministic Policy Gradient (DDPG) and Graph Convolutiona ...

We propose Hard Directional Graph Networks (HDGN), a point cloud model that both learns directional weight matrices and assigns a single matrix to each neighbor, achieving directional convolutions at ...

The adjacency matrix is a square matrix used to represent a finite graph, with rows and columns labeled by the graph's vertices. In the directed adjacency matrix, the entry in the ith row and jth ...

In C programming, graphs and matrices are often used to represent relationships between elements. A graph is a set of nodes (or vertices) connected by edges. A matrix can represent a graph, showing ...

We present an algorithm to estimate a single entry of the inverse of a matrix; it is derived using a bidirectional search on a flow graph. This method has immediate application in analyzing dynamical ...

The graph below shows the total number of publications each year in Quantum Graphs and Random Matrix Theory in Chaotic Systems.