Most people learn the Normal distribution as a "bell curve." This playground ignores the curve and focuses entirely on the Score—the vector field that tells a point where to go.
- No Inertia: Experience first-order dynamics where
$v = \text{score}(x)$ , leading to perfect, non-oscillatory convergence. - Anisotropy: Warp the space with Covariance (Sigma) and Rotation to see how paths bend.
- Nonlinear Residuals: Layer cubic terms over the linear Gaussian core to see how "real-world" scoring deviates from the ideal.
- Physics Comparison: Contrast score-based transport with robotic constant-speed motion and second-order spring physics.