Nogueira, XesúsFernández-Fidalgo, JavierRamos, LucíaCouceiro, IvánRamírez, Luis2024-07-052024-07-052024-08-15X. Nogueira, J. Fernández-Fidalgo, L. Ramos, I. Couceiro, and L. Ramírez, "Machine learning-based WENO5 scheme", Computers & Mathematics with Applications, Vol. 168, 15 Aug. 2024, pp. 84-99, doi: 10.1016/j.camwa.2024.05.0310898-1221http://hdl.handle.net/2183/37748[Abstract]: Machine learning (ML) is becoming a powerful tool in Computational Fluid Dynamics (CFD) to enhance the accuracy, efficiency, and automation of simulations. Currently, in the design of shock-capturing methods, there is still a heavy reliance on the expertise and scientific knowledge of each author, particularly in nonlinear components such as smoothness indicators and weighting functions. ML has the potential to reduce this dependency, since by leveraging large datasets, they can learn intricate patterns and make accurate predictions of these functions. In this work we present a neural network that compute the weighting functions in the WENO5 scheme. The proposed WENO5-NN scheme generalizes well for different resolutions, and in most of the cases tested, it outperforms the classical WENO5-JS scheme.engAtribución 3.0 Españahttp://creativecommons.org/licenses/by/3.0/es/Euler equationsFinite differencesMachine learningNeural networksWENOjournal articleopen access10.1016/j.camwa.2024.05.031