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Flow matching learns a time-dependent vector field v_t(x, c) whose ODE transports simple noise to complex data, enabling fast, deterministic sampling.
A forward stochastic differential equation (SDE) models a state that drifts deterministically and is shaken by random Brownian noise over time.