Active Learning Enhanced Surrogate Modeling of Jet Engines in JuliaSim
Computational Engineering, Finance, and Science
2025-01-15 v1
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.
Cite
@article{arxiv.2501.07701,
title = {Active Learning Enhanced Surrogate Modeling of Jet Engines in JuliaSim},
author = {Anas Abdelrehim and Dhairya Gandhi and Sharan Yalburgi and Ashutosh Bharambe and Ranjan Anantharaman and Chris Rackauckas},
journal= {arXiv preprint arXiv:2501.07701},
year = {2025}
}