Working paper

An in-vivo validation of ESI methods with focal sources

Electrical source imaging (ESI) aims at reconstructing the electrical brain activity from measurements of the electric field on the scalp. Even though the localization of single focal sources should be relatively straightforward, different methods provide diverse solutions due to the different…

Inverting the Fundamental Diagram and Forecasting Boundary Conditions: How Machine Learning Can Improve Macroscopic Models for Traffic Flow

In this paper, we aim at developing new methods to join machine learning techniques and macroscopic differential models for vehicular traffic estimation and forecast. It is well known that data-driven and model- driven approaches have (sometimes complementary) advantages and drawbacks. We consider…

Algorithm for the numerical assessment of the conjecture of a subglacial lake at Svalbard, Spitzbergen

The melting of glaciers coming with climate change threatens the heritage of the last glaciation of Europe likely contained in subglacial lakes in Greenland and Svalbard. This aspect urges specialists to focus their studies (theoretical, numerical and on-field) on such fascinating objects. Along…

AMG preconditioners for Linear Solvers towards Extreme Scale

Linear solvers for large and sparse systems are a key element of scientific applications, and their efficient implementation is necessary to harness the computational power of current computers. Algebraic Multigrid (AMG) Preconditioners are a popular ingredient of such linear solvers; this is the…

Network Clustering by Embedding of Attribute-augmented Graphs

In this paper we propose a new approach to detect clusters in undirected graphs with attributed vertices. The aim is to group vertices which are similar not only in terms of structural connectivity but also in terms of attribute values. We incorporate structural and attribute similarities between…

Energy-preserving splitting integrators for sampling from Gaussian distributions with Hamiltonian Monte Carlo method

The diffusive behaviour of simple random-walk proposals of many Markov Chain Monte Carlo (MCMC) algorithms results in slow exploration of the state space making inefficient the convergence to a target distribution. Hamiltonian/Hybrid Monte Carlo (HMC), by introducing fictious momentum variables,…

Alya towards Exascale: Algorithmic Scalability using PSCToolkit

In this paper, we describe some work aimed at upgrading the Alya code with up-to-date parallel linear solvers capable of achieving reliability, efficiency, and scalability in the computation of the pressure field at each time step of the numerical procedure for solving an LES formulation of the…

IMPROVING SOLVE TIME OF AGGREGATION-BASED ADAPTIVE AMG

This paper proposed improving the solve time of the bootstrap AMG proposed previously by the authors. This is achieved by incorporating the information, set of algebraically smooth vectors, generated by the bootstrap algorithm, in a single hierarchy by using sufficiently large aggregates, and these…

the hilbert transform in signal processing

Convergence of new quadrature rules for approximating the Hilbert transform are given. Numerical tests show the goodness of such approximations

Why diffusion-based preconditioning of Richards equation works: spectral analysis and computational experiments at very large scale.

We consider here a cell-centered finite difference approximation of the Richards equation in three dimensions, averaging for interface values the hydraulic conductivity, a highly nonlinear function, by arithmetic, upstream and harmonic means. The nonlinearities in the equation can lead to changes…