Gaussian Process Imputation of Multiple Financial Series. - Taco de Wolff; Alejandro Cuevas; Felipe A. Tobar (ICASSP, 2020)

Towards Optimally Weighted Physics-Informed Neural Networks in Ocean Modelling. - Taco de Wolff; Hugo Carrillo Lincopi; Luis Martí; Nayat Sánchez-Pi (CoRR, 2021)

MOGPTK: The multi-output Gaussian process toolkit. - Taco de Wolff; Alejandro Cuevas; Felipe A. Tobar (Neurocomputing, 2021)

MOPINNs: an evolutionary multi-objective approach to physics-informed neural networks. - Taco de Wolff; Hugo Carrillo Lincopi; Luis Martí; Nayat Sánchez-Pi (GECCO Companion, 2022)

Optimal Architecture Discovery for Physics-Informed Neural Networks. - Taco de Wolff; Hugo Carrillo Lincopi; Luis Martí; Nayat Sánchez-Pi (IBERAMIA, 2022)

Computationally-efficient initialisation of GPs: The generalised variogram method. - Felipe A. Tobar; Elsa Cazelles; Taco de Wolff (Trans. Mach. Learn. Res., 2023)

FairTrees: A Deep Learning Approach for Identifying Deforestation on Satellite Images. - Hernan Lira; Taco de Wolff; Luis Martí; Nayat Sánchez-Pi (IBERAMIA, 2024)

Detection of Manatee Vocalisations Using the Audio Spectrogram Transformer. - Stefano Schiappacasse; Taco de Wolff; Yann Henaut; Regina Cervera; Aviva Charles; Felipe A. Tobar (MLSP, 2024)

Evolutionary multi-objective physics-informed neural networks: The MOPINNs approach. - Hugo Carrillo Lincopi; Taco de Wolff; Luis Martí; Nayat Sánchez-Pi (AI Commun., 2024)