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Transitioning from Dirac mass initial conditions, this framework achieves robust approximations of Fokker-Planck solutions, even in the face of numerical instabilities.
Forget Kalman filters: AFSF leverages conditional normalizing flows to amortize Bayesian filtering and smoothing in high-dimensional nonlinear systems, enabling accurate trajectory estimation and extrapolation.
