Higher-Order Geometric Updates for Levenberg-Marquardt Method via Riemann Normal Coordinates

Nonlinear least-squares optimization is central to regression, physics-informed neural networks, and other machine-learning tasks. Such problems have a natural geometric interpretation, model predictions form a manifold in data space, while the chosen parameterization can introduce parameter-effects...