Nikolaos M. Matzakos

Error in long-term predictions can be managed to grow linearly rather than double exponentially, transforming how we approach dynamical system modeling with neural networks.

Activation saturation in Neural ODEs acts as a fundamental governor, preventing both chaotic sensitivity and strong contraction, regardless of training.