Efficient Inverse Design Optimization through Multi-fidelity
  Simulations, Machine Learning, and Search Space Reduction Strategies
Luka Grbcic Juliane Müller Wibe Albert de Jong
This paper introduces a methodology designed to augment the inverse design
optimization process in scenarios constrained by limited compute, through the
strategic synergy of multi-fidelity evaluations, machine learning models, and
optimization algorithms. The proposed methodology is analyzed on two distinct
engineering inverse design problems: airfoil inverse design and the scalar
field reconstruction problem. It leverages a machine learning model trained
with low-fidelity simulation data, in each optimization cycle, thereby
proficiently predicting a target variable and discerning whether a
high-fidelity simulation is necessitated, which notably conserves computational
resources. Additionally, the machine learning model is strategically deployed
prior to optimization to reduce the search space, thereby further accelerating
convergence toward the optimal solution. The methodology has been employed to
enhance two optimization algorithms, namely Differential Evolution and Particle
Swarm Optimization. Comparative analyses illustrate performance improvements
across both algorithms. Notably, this method is adeptly adaptable across any
inverse design application, facilitating a harmonious synergy between a
representative low-fidelity machine learning model, and high-fidelity
simulation, and can be seamlessly applied across any variety of
population-based optimization algorithms.