Learning Chaotic Dynamics through Second-Order Geometric Supervision

ArXi:2606.01596v1 Announce Type: cross Learning chaotic dynamical systems from data requires than short-term predictive accuracy: the learned model must preserve the attractor geometry and its invariant statistics. Trajectory (zero-order) and Jacobian (first-order) matching supervise the values and tangent structure of the vector field, but neither constrains how the field bends away from its tangent plane.