Unverified
2026
2p+1 Random Fourier Dynamics Loss
Train a parametric neural dynamical model by matching randomized Fourier features of observed and simulated trajectory windows, using k=2p+1 features when the model has p trainable dynamic parameters. The random projections compress long noisy trajectories into a small identification signal while retaining nonlinear dependence on all lags, potentially making model calibration less sensitive to correlated, non-Gaussian, or state-dependent observation noise.
Paper: Dynamic models with $p$ parameters are identified by $2p+1$ random features
arXiv:2607.16035