Time Relaxation with Iterative Modified Lavrentiev Regularization
For researchers in computational simulation and regularization, this work offers an incremental improvement by combining two existing techniques to enhance efficiency.
The paper proposes a time relaxation model with iterative modified Lavrentiev regularization to drive unresolved fluctuations to zero exponentially faster, enabling better approximation with fewer computational steps. It provides insight into parameter selection by analyzing how the relaxation term truncates solution scales.
A new time relaxation model with iterative modified Lavrentiev regularization method is studied. The aim of the relaxation term is to drive the unresolved fluctuations in a computational simulation to zero exponentially faster by an appropriate and often problem-dependent choice of its time relaxation parameter; together with iterative modified Lavrentiev regularization, the model will give a better approximation through de-convolution with fewer steps to compute. The goal of this paper herein is to understand how this time relaxation term acts to truncate solution scales and to use this understanding to give some helpful insight into parameter selection.