Bristol, London and Washington, D.C, 26 March 2025: Quantum algorithms company Phasecraft said it has developed a novel approach to quantum simulation that improves efficiency while cutting ...

Based on our results, we recommend Graph Neural Networks for physics simulation workloads. The TF-GNN API has a steep initial learning curve for using its data layer, especially if there is no data ...

The simulation experimental results show that the HS-SBOA algorithm demonstrates significant advantages in terms of convergence speed and accuracy, thereby validating the effectiveness of its improved ...

The envisaged algorithms are numerical solvers based on graph structures. In this article, we focus on kinematics and dynamics algorithms, but examples such as message passing on probabilistic ...

This is the case with an important problem in computer science called "graph isomorphism testing" whereby scientists use algorithms to test whether two graphs are the same.

The proposed solution, Trajectory Flow Matching (TFM), introduces an alignment-focused approach to model patient data. The innovation behind such a framework is to truly capture continuous-time ...

The algorithm works in an abstracted road map called a graph: a network of interconnected points (called vertices) in which the links between vertices are labeled with numbers (called weights). These ...

Synergism of Computational Simulation Technique and Machine Learning Algorithm for Prediction of Anticorrosion Properties of Some Antipyrine Derivatives ...

University of Virginia School of Engineering and Applied Science professor Nikolaos Sidiropoulos has introduced a breakthrough in graph mining with the development of a new computational algorithm.