Problem we are facing: Because of the massive computational burden of typical inverse CFD design process, searching a wide variety of input geometry shape to optimize a payoff function (e.g. drag) is infeasible.
How we aim to tackle it: To reduce the search space for new optimized shapes, we leverage the universal approximation property of deep neural networks to estimate a differentiable surrogate to the CFD forward simulation: CFD-Net.
McLaren and Boeing have already
expressed their interest to work
with us on this line of research.
Investigator:
