# Machine Learning Methods for the Design and Operation of Liquid Rocket Engines -- Research Activities at the DLR Institute of Space Propulsion

arXiv: https://arxiv.org/abs/2102.07109 · HTML: https://mlsanity.com/paper/2102.07109/

## Key facts

- Cited by 3 papers in the map (data through 14 Oct 2026).
- Builds on 4 of its 25 references that are in the map.
- First posted on arXiv 14 Feb 2021.

## Builds on (most cited first, 4 of 4)

- [A Review of Uncertainty Quantification in Deep Learning: Techniques, Applications and Challenges](https://mlsanity.com/paper/2011.06225/) (2020)
- [A Reinforcement Learning Approach for Transient Control of Liquid Rocket Engines](https://mlsanity.com/paper/2006.11108/) (2020)
- [Heat Transfer Prediction for Methane in Regenerative Cooling Channels with Neural Networks](https://mlsanity.com/paper/1907.11281/) (2019)
- [Early Detection of Thermoacoustic Instabilities in a Cryogenic Rocket Thrust Chamber using Combustion Noise Features and Machine Learning](https://mlsanity.com/paper/2011.14985/) (2020)

## Built on by (most cited first, 3 of 3)

- [Reducing Experimental Testing in Space Propulsion Film Cooling Analyses by Pixelwise Generative Image Interpolation](https://mlsanity.com/paper/2605.29911/) (2026)
- [Interactive Design-of-Experiments: Optimizing a Cooling System](https://mlsanity.com/paper/2408.12607/) (2024)
- [Airfoil2Vec: Spectral Geometry-Conditioned Neural Surrogate Models for Airfoil Aerodynamics and a Downforce-Generating CFD Dataset](https://mlsanity.com/paper/2609.38213/) (2026)

## Abstract (arXiv)

The last years have witnessed an enormous interest in the use of artificial intelligence methods, especially machine learning algorithms. This also has a major impact on aerospace engineering in general, and the design and operation of liquid rocket engines in particular, and research in this area is growing rapidly. The paper describes current machine learning applications at the DLR Institute of Space Propulsion. Not only applications in the field of modeling are presented, but also convincing results that prove the capabilities of machine learning methods for control and condition monitoring are described in detail. Furthermore, the advantages and disadvantages of the presented methods as well as current and future research directions are discussed.