On the importance of structural identifiability for machine learning with partially observed dynamical systems
The episode discusses structural identifiability, a concept crucial for machine learning applied to partially observed dynamical systems. Hosts explain that standard ML often wrongly assumes perfect observability. The paper provides a necessary pre-training check—a 'litmus test'—to ensure the model structure can logically represent the real system before any predictions are made.