אירועים והרצאות בפקולטה למדעי המחשב ע"ש הנרי ומרילין טאוב

Koopman-based methods for time-series forecasting model nonlinear dynamics as linear evolution in a latent space, but deterministic formulations often mix noise with the underlying dynamics, limiting long-term accuracy. This talk introduces KoopSDE, which extends Koopman models with a latent stochastic differential equation.

By linking the drift to the Koopman generator and learning a diffusion term, the method separates dynamics from noise and improves robustness in long-horizon forecasting.