Adjoint Methods for Guiding Adaptive Mesh Refinement in Tsunami Modeling

TL;DR

This paper introduces an adjoint-based approach integrated into adaptive mesh refinement for tsunami modeling, enabling targeted refinement around regions of interest, reducing computational costs while maintaining accuracy.

Contribution

It presents a novel method combining adjoint equations with adaptive mesh refinement in GeoClaw, improving efficiency in tsunami simulations targeting specific areas.

Findings

Targeted refinement reduces computational time.

Accuracy of solutions is maintained.

Adjoint method integration improves efficiency.

Abstract

One difficulty in developing numerical methods for tsunami modeling is the fact that solutions contain regions where much higher resolution is required than elsewhere in the domain, particularly since the solution may contain discontinuities or other localized features. The Clawpack software deals with this issue by using block-structured adaptive mesh refinement to selectively refine around propagating waves. For problems where only a target area of the total solution is of interest (e.g. one coastal community), a method that allows identifying and refining the grid only in regions that influence this target area would significantly reduce the computational cost of finding a solution. In this work, we show that solving the time-dependent adjoint equation and using a suitable inner product with the forward solution allows more precise refinement of the relevant waves. We present examples solving the shallow water equations in one and two dimensions. To perform these simulations, the use of the adjoint method has been integrated into the adaptive mesh refinement strategy of the open source GeoClaw software. We also present results that show that the accuracy of the solution is maintained and the computational time required is significantly reduced through the integration of the adjoint method into adaptive mesh refinement.