Universal set of Observables for Forecasting Physical Systems through Causal Embedding
The authors introduce causal embedding, a method to uniquely represent left-infinite system orbits using a pair of elements in a different space. This allows learning functions that enable universal forecasting and robustly stabilize noisy trajectories, overcoming limitations in traditional reservoir computing methods.