Neural networks for multifidelity surrogate modelling
In this talk we discuss how neural networks can be used together with multifidelity techniques to accelerate the parametric solution of time-dependent partial differential equations. Based on a small number of high-resolution (high-fidelity) simulations, a neural network is trained ('offline') that maps low-fidelity simulation outputs to achieve an accuracy similar to the high-fidelity simulations in 'online' mode. We show promising results for non-linear equations including the flow over a backward-facing step.
