2 citations · 3 across the 3 of their papers we have counts for
3 papers
Gauss-Newton Natural Gradient Descent for Physics-Informed Computational Fluid Dynamics
Anas Jnini, Flavio Vella, Marius Zeinhofer
We propose Gauss-Newton's method in function space for the solution of the Navier-Stokes equations in the physics-informed neural network (PINN) framework. Upon discretization, thi…
Scaling Expected Force: Efficient Identification of Key Nodes in Network-based Epidemic Models
Paolo Sylos Labini, Andrej Jurco, Matteo Ceccarello +3
Centrality measures are fundamental tools of network analysis as they highlight the key actors within the network. This study focuses on a newly proposed centrality measure, Expect…
ProbGraph: High-Performance and High-Accuracy Graph Mining with Probabilistic Set Representations
Maciej Besta, Cesare Miglioli, Paolo Sylos Labini +11
Important graph mining problems such as Clustering are computationally demanding. To significantly accelerate these problems, we propose ProbGraph: a graph representation that enab…