High-Dimensional Bayesian Optimisation with Large-Scale Constraints --
  An Application to Aeroelastic Tailoring
Hauke Maathuis Roeland De Breuker Saullo G. P. Castro
Design optimisation potentially leads to lightweight aircraft structures with
lower environmental impact. Due to the high number of design variables and
constraints, these problems are ordinarily solved using gradient-based
optimisation methods, leading to a local solution in the design space while the
global space is neglected. Bayesian Optimisation is a promising path towards
sample-efficient, global optimisation based on probabilistic surrogate models.
While Bayesian optimisation methods have demonstrated their strength for
problems with a low number of design variables, the scalability to
high-dimensional problems while incorporating large-scale constraints is still
lacking. Especially in aeroelastic tailoring where directional stiffness
properties are embodied into the structural design of aircraft, to control
aeroelastic deformations and to increase the aerodynamic and structural
performance, the safe operation of the system needs to be ensured by involving
constraints resulting from different analysis disciplines. Hence, a global
design space search becomes even more challenging. The present study attempts
to tackle the problem by using high-dimensional Bayesian Optimisation in
combination with a dimensionality reduction approach to solve the optimisation
problem occurring in aeroelastic tailoring, presenting a novel approach for
high-dimensional problems with large-scale constraints. Experiments on
well-known benchmark cases with black-box constraints show that the proposed
approach can incorporate large-scale constraints.