Uncertainty Quantification Seminar Yous van Halder (CWI)

Neural networks for multifidelity surrogate modelling

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.