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Tensorial Implementation for Robust Variational Physics-Informed Neural Networks

Vilkha, A.; Uriarte, C.; Maczuga, P.; Służalec, T.; Paszyński, M. (2025-01-01)

Variational Physics-Informed Neural Networks (VPINN) train the parameters of neural networks (NN) to solve partial differential equations (PDEs). They perform unsupervised training based on the physical laws described by the...

Transforming Combinatorial Optimization Problems in Fourier Space: Consequences and Uses

Elorza, A.; Benavides, X.; Ceberio, J.; Hernando, L.; Lozano, J.A. (2024-09-10)

We analyze three permutation-based combinatorial optimization problems in Fourier space, namely, the quadratic assignment problem, the linear ordering problem (LOP), and the symmetric and nonsymmetric traveling salesperson p...

Learning the Graph Structure of Regular Vine-Copulas from Dependence Lists

Carrera, D.; Santana, R.; Lozano, J.A. (2025-01-01)

Regular vine copulas (R-vines) provide a comprehensive framework for modeling high- dimensional dependencies using a hierarchy of trees and conditional pair-copulas. While the graphical structure of R-vines is traditionall...

Self-Composing Policies for Scalable Continual Reinforcement Learning

Vadillo, J.; Santana, R.; Lozano, J.A. (2025-01-01)

Reliable deployment of machine learning models such as neural networks continues to be challenging due to several limitations. Some of the main shortcomings are the lack of interpretability and the lack of robustness again...