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 co...

Abstract Model order reduction (MOR) methods enable the generation of real-time-capable digital twins, with potential to unlock various novel value streams in industry. While traditional projection-based are robust and accurate for linear problems, incorporating machine learning deal nonlinearity becomes a new choice reducing complex problems. These kinds independent numerical solver full model...

Abstract We consider a second‐order linear system of ordinary differential equations (ODEs) including random variables. A stochastic Galerkin method yields larger deterministic ODEs. apply model order reduction (MOR) this high‐dimensional dynamical system, where its internal energy represents quadratic quantity interest. investigate the properties MOR with respect to stability, passivity and di...