This is joint work with Rok Cestnik — many thanks for a great collaboration!

The method performs well for attractor reconstruction, bifurcation diagram estimation, and prediction with partial or noisy data. Interestingly, adding small noise during training improves long-term stability — promising for surrogate models and digital twins.

We introduce a scalable next-generation reservoir computing (NGRC) framework based on a pseudorandom nonlinear projection of time-delay embedded inputs. This decouples feature-space dimensionality from observation size, giving much more flexibility.

Sounds like I missed out on a great session!

We should use it as a talk title. Or give a presentation at some meditation retreat.