Before revision: While reduced-order models (ROMs) have demonstrated success in many applications across computational science, challenges remain when applied both to extreme-scale models due to the prohibitive cost of generating requisite training data, and to decomposable systems due to many-query problems often requiring repeated reconfigurations of system components. Therefore, we propose the domain-decomposition least-squares Petrov-Galerkin (DD-LSPG) model-reduction method applicable to parameterized systems of nonlinear algebraic equations .
After revision: A novel domain-decomposition least-squares Petrov-Galerkin (DD-LSPG) model-reduction method applicable to parameterized systems of nonlinear algebraic equations .