# Active Learning Enhanced Surrogate Modeling of Jet Engines in JuliaSim

arXiv: https://arxiv.org/abs/2501.07701 · HTML: https://mlsanity.com/paper/2501.07701/

## Key facts

- Cited by 1 papers in the map (data through 14 Oct 2026).
- First posted on arXiv 13 Jan 2025.

## Built on by (most cited first, 1 of 1)

- [Learning Gradient Flow: Using Equation Discovery to Accelerate Engineering Optimization](https://mlsanity.com/paper/2602.13513/) (2026)

## Abstract (arXiv)

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.