Work

Neural networks for parameter estimation in geostatistical models with geometric anisotropies

Year: 2025

Type: article

Abstract: This article presents two neural network approaches for estimating the covariance function of a spatial Gaussian random field defined in a portion of the euclidean plane. Our proposal builds upon recent contributions, expanding from the purely isotropic setting to encompass geometrically anisotropic correlation structures, i.e. random fields with correlation ranges that vary across different directions. We conduct experiments with both simulated and real data to assess the performance of the methodology and to provide guidelines to practitioners.

Source: Machine Learning Science and Technology

DOI: 10.1088/2632-2153/adcdc2

Authors: Alejandro Villazón, Alfredo Alegría, Xavier Emery

Institutions: Universidad Técnica Federico Santa María, Pontificia Universidad Católica de Chile, University of Chile

Language: English

Topic: Gaussian Processes and Bayesian Inference

Subfield: Artificial Intelligence

Field: Computer Science

Domain: Physical Sciences

Open Access status: gold

Estimated APC paid: $3,000