Two-Sided Projection Methods for Nonlinear Model Order Reduction

In this paper, we investigate a recently introduced approach for nonlinear model order reduction based on generalized moment matching. Using basic tensor calculus, we propose a computationally efficient way of computing reduced-order models. We further extend the idea of two-sided interpolation methods to this more general setting by employing the tensor structure of the Hessian. We investigate the use of oblique projections in order to preserve important system properties such as stability. We test one-sided and two-sided projection methods for different semi-discretized nonlinear partial differential equations and show their competitiveness when compared to proper orthogonal decomposition (POD).