Efficient Wildland Fire Simulation via Nonlinear Model Order Reduction

We propose a new hyper-reduction method for recently introduced nonlinear model reduction framework based on dynamically transformed basis functions and especially well-suited advection-dominated systems. Furthermore, we discuss applying this to wildland fire whose dynamics feature traveling combustion waves local ignition is thus challenging classical schemes linear subspaces. The allows us construct parameter-dependent reduced-order models (ROMs) with efficient offline/online decomposition. numerical experiments demonstrate that the ROMs obtained by novel outperform those approach using proper orthogonal decomposition discrete empirical interpolation in terms of run time accuracy.