Nonlinear discontinuous Petrov-Galerkin methods
This work extends the dPG framework to nonlinear problems, offering rigorous error analysis for practitioners in computational PDEs.
The paper introduces a nonlinear discontinuous Petrov-Galerkin method for a nonlinear uniformly convex model problem, providing quasi-optimal a priori and reliable a posteriori error estimates with built-in guaranteed error control despite inexact solve.
The discontinuous Petrov-Galerkin method is a minimal residual method with broken test spaces and is introduced for a nonlinear model problem in this paper. Its lowest-order version applies to a nonlinear uniformly convex model example and is equivalently characterized as a mixed formulation, a reduced formulation, and a weighted nonlinear least-squares method. Quasi-optimal a priori and reliable and efficient a posteriori estimates are obtained for the abstract nonlinear dPG framework for the approximation of a regular solution. The variational model example allows for a built-in guaranteed error control despite inexact solve. The subtle uniqueness of discrete minimizers is monitored in numerical examples.