Tim Keil, Mario Ohlberger, Felix Schindler, Julia Schleuß
Research article in edited proceedings (conference) | Peer reviewedTo efficiently tackle parametrized multi and/or large scale problems, we propose an adaptive localized model order reduction framework combining both local offline training and local online enrichment with localized error control. For the latter, we adapt the residual localization strategy introduced in [Buhr, Engwer, Ohlberger, Rave, SIAM J. Sci. Comput., 2017] which allows to derive a localized a posteriori error estimator that can be employed to adaptively enrich the reduced solution space locally where needed. Numerical experiments demonstrate the potential of the proposed approach.
Keil, Tim | Professorship of Applied Mathematics, especially Numerics (Prof. Ohlberger) |
Ohlberger, Mario | Professorship of Applied Mathematics, especially Numerics (Prof. Ohlberger) Center for Nonlinear Science Center for Multiscale Theory and Computation |
Schindler, Felix Tobias | Professorship of Applied Mathematics, especially Numerics (Prof. Ohlberger) |
Schleuß, Julia | Professorship of Applied Mathematics, especially Numerics (Prof. Ohlberger) |