This project aims to push theoretical insight and practical acceleration for flow-based generative models. This project aims to accelerate Flow Matching models by integrating second-order dynamics, incorporating both velocity and acceleration during training. This enhances alignment between learned flows and data geometry. In parallel, we analyze trajectory evolution during sampling across solvers and step sizes, aiming to design faster, trajectory-consistent sampling methods with reduced NFE and strong generation quality.