4 papers
Coarse reduced model selection for nonlinear state estimation
James A. Nichols
State estimation is the task of approximately reconstructing a solution of a parametric partial differential equation when the parameter vector is unknown and the only info…
Nonlinear reduced models for state and parameter estimation
Albert Cohen, Wolfgang Dahmen, Olga Mula +1
State estimation aims at approximately reconstructing the solution to a parametrized partial differential equation from linear measurements, when the parameter vector i…
Optimal reduced model algorithms for data-based state estimation
Albert Cohen, Wolfgang Dahmen, Ron DeVore +3
Reduced model spaces, such as reduced basis and polynomial chaos, are linear spaces of finite dimension which are designed for the efficient approximation of families par…
Reduced Basis Greedy Selection Using Random Training Sets
Albert Cohen, Wolfgang Dahmen, Ronald DeVore
Reduced bases have been introduced for the approximation of parametrized PDEs in applications where many online queries are required. Their numerical efficiency for such problems h…