A Regularized Auxiliary Variable (RAV) Approach for Gradient Flows
For researchers working on numerical methods for gradient flows, this provides a more robust and accurate time-discrete scheme with theoretical guarantees.
The paper proposes a regularized auxiliary variable (RAV) approach for gradient flows, achieving unconditional energy stability and optimal error estimates without time-step restrictions, with numerical results showing improved accuracy over the SAV method.
In this paper, we propose a regularized auxiliary variable (RAV) approach and construct accurate and robust time-discrete schemes for a large class of gradient flows. By introducing an auxiliary variable $r=0$ and constructing an auxiliary equation that naturally fits into the energy relation, the numerical solution $r^{n+1}$ of the auxiliary variable is corrected at each time step to preserve consistency with the original system. The developed RAV scheme satisfies unconditional energy stability with respect to the original variables, and in certain cases the original energy law can be directly recovered. Furthermore, we obtain a uniform bound on the norm of the numerical solution, which allows us to establish the optimal error estimate in $L^\infty(0,T;H^2)$ for the second-order scheme without any restriction on the time step. We present ample numerical results, including comparisons with the scalar auxiliary variable (SAV) approach, to demonstrate the accuracy and effectiveness of the proposed RAV approach.