By combining classical state estimation (RTS smoothing) with neural network learning, you can recover missing physics from partial observations while keeping known equations explicit—useful for scientific modeling where you have incomplete data and partial domain knowledge.
This paper proposes a hybrid approach that combines physics-based differential equations with neural networks to learn missing components of dynamical systems from incomplete measurements.