3 papers
cs.LG2026
Kolmogorov Arnold networks (KAN) for aerodynamic prediction: a comparison with MLPs and GNNs
Miguel Jaraiz, Fermin Gutierrez, Pablo Yeste +4
Kolmogorov Arnold networks (KAN) have recently been introduced as a (deep) neural network architecture whose trainable parameters adapt the activation functions, instead of the coe…
cs.LG2025
Reliable Statistical Guarantees for Conformal Predictors with Small Datasets
Miguel Sánchez-DomÃnguez, Lucas Lacasa, Javier de Vicente +2
Surrogate models (including deep neural networks and other machine learning algorithms in supervised learning) are capable of approximating arbitrarily complex, high-dimensional in…
cs.LG2025
A Certifiable Machine Learning-Based Pipeline to Predict Fatigue Life of Aircraft Structures
Ãngel Ladrón, Miguel Sánchez-DomÃnguez, Javier Rozalén +5
Fatigue life prediction is essential in both the design and operational phases of any aircraft, and in this sense safety in the aerospace industry requires early detection of fatig…