4 papers
Reliable Statistical Guarantees for Conformal Predictors with Small Datasets
Miguel Sánchez-Domínguez, 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…
A Certifiable Machine Learning-Based Pipeline to Predict Fatigue Life of Aircraft Structures
Ángel Ladrón, Miguel Sánchez-Domínguez, 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…
Transfer learning-enhanced deep reinforcement learning for aerodynamic airfoil optimisation subject to structural constraints
David Ramos, Lucas Lacasa, Eusebio Valero +1
The main objective of this paper is to introduce a transfer learning-enhanced deep reinforcement learning (DRL) methodology that is able to optimise the geometry of any airfoil bas…
Towards certification: A complete statistical validation pipeline for supervised learning in industry
Lucas Lacasa, Abel Pardo, Pablo Arbelo +8
Methods of Machine and Deep Learning are gradually being integrated into industrial operations, albeit at different speeds for different types of industries. The aerospace and aero…