On leveraging physical knowledge to augment neural network-based surrogate models for simulations

The computational cost of high-fidelity engineering simulations, for example CFD, is prohibitive if the application requires frequent design iterations or even fully fledged optimization. A popular way to reduce the computational cost and enable fast iteration cycles is to use surrogate models that are trained to predict simulation results from...