Simulation-informed gaussian processes for accelerated Bayesian optimisation

Bayesian optimisation, known for its minimal requirement of design parameter evaluations, is vital for global industrial process optimisation. It typically employs Gaussian processes as surrogate models for specific objectives. Traditional Bayesian optimisation is directly applied to the actual system. However, numerous industrial applications rely on simulation models. Although these models fail to represent the real system fully, they offer valuable insights to improve optimisation algorithms. A model that relies on the transfer of physical knowledge from the simulations to a Gaussian process model of the actual system is called a physics-informed Gaussian process model. Inspired by this, this chapter proposes a novel approach called simulation-informed Gaussian process. This approach constructs a Gaussian process kernel from simulation results to better capture design parameter-objective function correlations. This results in an accelerated Bayesian optimisation convergence of the actual system. We show this by comparing our method to conventional and physics-informed Bayesian optimisation. In addition, we offer insights into the consequences of integrating potentially misleading information into the Gaussian process framework.