# Towards On-Board Implementation of ML-Based Helicopter Weight Estimator

arXiv: https://arxiv.org/abs/2608.19210 · HTML: https://mlsanity.com/paper/2608.19210/

## Key facts

- No citing papers in the map yet (data through 14 Oct 2026).
- Builds on 3 of its 20 references that are in the map.
- First posted on arXiv 12 Jun 2026.

## Builds on (most cited first, 3 of 3)

- [On the Effectiveness of Interval Bound Propagation for Training Verifiably Robust Models](https://mlsanity.com/paper/1810.12715/) (2018)
- [Formal description of ML models for unambiguous implementation](https://mlsanity.com/paper/2307.12713/) (2023)
- [Implementation of airborne ML models with semantics preservation](https://mlsanity.com/paper/2509.18681/) (2025)

## Abstract (arXiv)

This paper focuses on the implementation of a novel supervised Machine Learning model for estimating helicopter weight during takeoff, utilizing extensive datasets from Airbus's global in-service fleet. The study details a learning assurance process aligned with the EASA concept paper for machine learning application, and with the on-going Eurocae ED-324. We propose a set of Machine Learning Requirements, a Machine Learning Model Description, and its implementation for a long short-term memory recurrent neural network. Finally, we verify the requirements on the implementation. Demonstrated on legacy avionics computers, the implementation is suitable for the deployment of the developed Machine Learning Model weight estimator on airborne targets for critical functions such as on-board alerting.