Implementation of airborne ML models with semantics preservation
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arXiv
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| Autores principales: | , , , , |
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| Formato: | Preprint |
| Publicado: |
2025
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| Acceso en línea: | |
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| _version_ | 1866915508549320704 |
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| author | Valot, Nicolas Fabre, Louis Lesage, Benjamin Mechouche, Ammar Pagetti, Claire |
| author_facet | Valot, Nicolas Fabre, Louis Lesage, Benjamin Mechouche, Ammar Pagetti, Claire |
| contents | Machine Learning (ML) may offer new capabilities in airborne systems. However, as any piece of airborne systems, ML-based systems will be required to guarantee their safe operation. Thus, their development will have to be demonstrated to be compliant with the adequate guidance. So far, the European Union Aviation Safety Agency (EASA) has published a concept paper and an EUROCAE/SAE group is preparing ED-324. Both approaches delineate high-level objectives to confirm the ML model achieves its intended function and maintains training performance in the target environment. The paper aims to clarify the difference between an ML model and its corresponding unambiguous description, referred to as the Machine Learning Model Description (MLMD). It then refines the essential notion of semantics preservation to ensure the accurate replication of the model. We apply our contributions to several industrial use cases to build and compare several target models. |
| format | Preprint |
| id |
arxiv_https___arxiv_org_abs_2509_18681 |
| institution | arXiv |
| publishDate | 2025 |
| record_format | arxiv |
| spellingShingle | Implementation of airborne ML models with semantics preservation Valot, Nicolas Fabre, Louis Lesage, Benjamin Mechouche, Ammar Pagetti, Claire Artificial Intelligence Machine Learning (ML) may offer new capabilities in airborne systems. However, as any piece of airborne systems, ML-based systems will be required to guarantee their safe operation. Thus, their development will have to be demonstrated to be compliant with the adequate guidance. So far, the European Union Aviation Safety Agency (EASA) has published a concept paper and an EUROCAE/SAE group is preparing ED-324. Both approaches delineate high-level objectives to confirm the ML model achieves its intended function and maintains training performance in the target environment. The paper aims to clarify the difference between an ML model and its corresponding unambiguous description, referred to as the Machine Learning Model Description (MLMD). It then refines the essential notion of semantics preservation to ensure the accurate replication of the model. We apply our contributions to several industrial use cases to build and compare several target models. |
| title | Implementation of airborne ML models with semantics preservation |
| topic | Artificial Intelligence |
| url | https://arxiv.org/abs/2509.18681 |