Author

Johann Maximilian Christensen

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#artificial intelligence Open access Apr 2026

From High-Dimensional Spaces to Verifiable ODD Coverage for Safety-Critical AI-based Systems

This paper proposes a method that integrates parameter discretization, constraint-based filtering, and criticality-based dimension reduction into a structured, multi-step ODD coverage verification process, advancing a Safety-by-Design approach while complying with EASA's standards.

Thomas Stefani, Johann Maximilian Christensen, Elena Hoemann et al. · International Conference on... · 1 citation

#artificial intelligence Jan 2026

Defining Operational Conditions for Safety-Critical AI-Based Systems from Data

This paper presents a novel method for defining the ODD a posteriori from previously collected data using a multidimensional kernel-based representation that supports future certification of data-driven, safety-critical AI-based systems.

Johann Maximilian Christensen, Elena Hoemann, F. Köster et al. · arXiv.org · 1 citation

Preprint Aug 2026

On the Applicability of Safety Nets: A Safety-By-Design Solution for Certifying Neural Networks

This work provides the first-ever open-source implementation of Safety Nets for HCAS and VCAS with replicable results, demonstrating a practical pathway toward certifiable AI-based systems in aviation and establishing Safety Nets as a viable Safety-by-Design solution for safety-critical applications.

J. Christensen, Thomas Stefani, Elena Hoemann et al. · 0 citations

Preprint Aug 2026

Coverage-Driven Verification for Safety-by-Design in AI-Based Collision Avoidance Systems

This work presents a method for representativeness assessment of AI/ML constituent ODDs in the context of aviation safety assurance and illustrates how statistical distribution comparison methods can support the assessment of representativeness for safety-critical AI applications.

Thomas Stefani, J. Christensen, Elena Hoemann et al. · 0 citations