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Reducing spatial discretization error on coarse CFD simulations using an openFOAM-embedded deep learning framework

Gonzalez-Sieiro, J.; Pardo, D.; Nava, V.; Calo, V.M.; Towara, M. (2024-09-09)

We propose a method for reducing the spatial discretization error of coarse computational fluid dynamics (CFD) problems by enhancing the quality of low-resolution simulations using deep learning. We feed the model with fine-...

Preserving the Fairness Guarantees of Classifiers in Changing Environments: a Survey

Barrainkua, A.; Gordaliza, P.; Lozano, J.A.; Quadrianto, N. (2023-01-01)

Uncertainty Matters: Stable Conclusions Under Unstable Assessment of Fairness Results

Barrainkua, A.; Gordaliza, P.; Lozano, J.A.; Quadrianto, N. (2024-01-01)

Recent studies highlight the effectiveness of Bayesian methods in assessing algorithm performance, particularly in fairness and bias evaluation. We present Uncertainty Matters, a multi-objective uncertainty-aware algorithmi...

Semi-analytical framework for the study of finite-time stability of forced dynamical systems with time varying parameters

Bartfai, A.; Ponce Vanegas, F.; Hogan, J.; Kuske, R.; Dombovari, Z. (2024-01-01)

We present a framework to analytically approximate the solution of forced dynamical systems with time varying parameters and to analyse their finite-time stability. The work was inspired by an example in robotic machining, w...