Sector 03 / Intersection
Aviation is the hardest possible environment for machine learning: safety-critical, heavily regulated and unforgiving of unexplained behaviour. That is exactly what makes this intersection interesting.
7 curated links
Certified autonomy is arriving through cargo and retrofit programmes first. The regulatory question is not whether the software can fly the aeroplane, but how you demonstrate a learned system behaves predictably across the full flight envelope.
Graph neural network forecasts now match or beat traditional numerical models at a fraction of the compute, which changes dispatch, fuel planning and turbulence avoidance.
Sensor streams from engines and airframes feed models that flag component degradation before it becomes an AOG event — the least glamorous and most commercially valuable use of AI in aviation.
Trajectory prediction, conflict detection and arrival sequencing are all being augmented by learned models under EUROCONTROL and FAA programmes.
Autonomy, AI-assisted air traffic and next-gen airframes.
How machine learning is entering European air traffic management.
Certification thinking for machine learning in safety-critical avionics.
Automated flight systems for existing cargo aircraft.
Electric air-taxi aircraft development, certification progress and flight testing.
Autonomous pilot systems targeting certified operations.
ML weather models now beating numerical forecasts — directly useful in dispatch.