Active Learning Enhanced Surrogate Modeling of Jet Engines in JuliaSim

TL;DR

This paper introduces an active learning method to develop highly accurate surrogate models of jet engines, achieving 0.1% relative error, significantly improving the efficiency and precision of design optimization processes.

Contribution

The paper presents a novel active learning approach for surrogate modeling that outperforms traditional methods in accuracy for complex turbofan engine simulations.

Findings

Achieved 0.1% relative error in surrogate models

Active learning outperforms brute-force data generation

Enhanced surrogate models enable faster design optimization

Abstract

Surrogate models are effective tools for accelerated design of complex systems. The result of a design optimization procedure using surrogate models can be used to initialize an optimization routine using the full order system. High accuracy of the surrogate model can be advantageous for fast convergence. In this work, we present an active learning approach to produce a very high accuracy surrogate model of a turbofan jet engine, that demonstrates 0.1\% relative error for all quantities of interest. We contrast this with a surrogate model produced using a more traditional brute-force data generation approach.