  Complex optimal design and control processes often require repeated
evaluations of expensive objective functions and consist of large design
spaces. Data-driven surrogates such as neural networks and Gaussian processes
provide an attractive alternative to simulations and are utilized frequently to
represent these objective functions in optimization. However, pure data-driven
models, due to a lack of adherence to basic physics laws and constraints, are
often poor at generalizing and extrapolating. This is particularly the case,
when training occurs over sparse high-fidelity datasets. A class of
Physics-infused machine learning (PIML) models integrate ML models with
low-fidelity partial physics models to improve generalization performance while
retaining computational efficiency. This paper presents two potential
approaches for Physics infused modelling of aircraft aerodynamics which
incorporate Artificial Neural Networks with a low-fidelity Vortex Lattice
Method model with blown wing effects (BLOFI) to improve prediction performance
while also keeping the computational cost tractable. This paper also develops
an end-to-end auto differentiable open-source framework that enables efficient
training of such hybrid models. These two PIML modelling approaches are then
used to predict the aerodynamic coefficients of a 6 rotor eVTOL aircraft given
its control parameters and flight conditions. The models are trained on a
sparse high-fidelity dataset generated using a CHARM model. The trained models
are then compared against the vanilla low-fidelity model and a standard pure
data-driven ANN. Our results show that one of the proposed architectures
outperforms all the other models at a nominal increase in run time. These
results are promising and pave way for PIML frameworks which can generalize
over different aircraft and configurations thereby significantly reducing costs
of design and control.
