New paper on MOR for nonlinear problems

Under the doi 10.3390/mca23010008 Felix Fritzen, Bernard Haasdonk, David Ryckelynck any myself have published a new paper on an algorithmic discussion of competing parametric model reduction techniques for nonlinear problems in Mathematical and Computational Applications. The Galerkin reduced basis (RB) formulation is presented, which fails at providing significant gains with respect to the computational efficiency for nonlinear problems. Renowned methods for the reduction of the computing time of nonlinear reduced order models are investigated. All approaches are applied to a simple uncertainty quantification of a planar nonlinear thermal conduction problem. The paper is free under CC BY 4.0.