3 papers
physics.flu-dyn2026
Gradient-free learning of a closed-loop wall controller for turbulent drag reduction
Giorgio Maria Cavallazzi, Miguel Pérez Cuadrado, Alfredo Pinelli
Closed-loop wall controllers learnt by multi-agent reinforcement learning are usually trained on periodic boxes far smaller than the flows they are meant to drive, and a large part…
physics.flu-dyn2026
Reward hacking in physical reinforcement learning revealed by turbulent drag reduction
Giorgio Maria Cavallazzi, Miguel Pérez-Cuadrado, Alfredo Pinelli
Reinforcement-learning controllers optimise specified rewards, but in physical systems those rewards often capture only part of the true control objective. Three mechanisms through…
physics.comp-ph2025
Walsh-Hadamard Neural Operators for Solving PDEs with Discontinuous Coefficients
Giorgio M. Cavallazzi, Miguel Pérez Cuadrado, Alfredo Pinelli
Neural operators have emerged as powerful tools for learning solution operators of partial differential equations (PDEs). However, standard spectral methods based on Fourier transf…