On the unmapped tent pitching for the heterogeneous wave equation
Marcella Bonazzoli (IDEFIX), Gabriele Ciaramella (MOX), Ilario Mazzieri (MOX)

TL;DR
This paper extends the Unmapped Tent Pitching (UTP) algorithm for solving the heterogeneous wave equation, demonstrating its efficiency in handling media with varying wave speeds without nonlinear mappings.
Contribution
It introduces strategies to adapt UTP for heterogeneous media, highlighting the most efficient approach using uniform space--time subdomains based on maximum wave speed.
Findings
UTP can be effectively extended to heterogeneous media.
Using uniform space--time subdomains improves computational efficiency.
The approach avoids nonlinear mappings, simplifying implementation.
Abstract
The Unmapped Tent Pitching (UTP) algorithm is a space--time domain decomposition method for the parallel solution of hyperbolic problems. It was originally introduced for the homogeneous one-dimensional wave equation in [Ciaramella, Gander, Mazzieri, 2024]. UTP is inspired by the Mapped Tent Pitching (MTP) algorithm [Gopalakrishnan, Sch{\"o}berl, Wintersteiger, 2017], which constructs the solution by iteratively building polytopal space--time subdomains, referred to as tents. In MTP, each physical tent is mapped onto a space--time rectangle, where local problems are solved before being mapped back to the original domain. In contrast, UTP avoids the nonlinear and potentially singular mapping step by computing the solution directly on a physical space--time rectangle that contains the tent, at the expense of redundant computations in the region outside the tent. In this work, we…
Peer Reviews
No public reviews on file for this paper yet. If you reviewed it on a platform where reviews are public (OpenReview, ICLR, NeurIPS, ICML), you can paste yours below so the community can read it here.
Videos
No videos yet. Explain this paper in a talk, walkthrough, or lecture? Add one.
Taxonomy
TopicsElectromagnetic Simulation and Numerical Methods · Advanced Numerical Methods in Computational Mathematics · Model Reduction and Neural Networks
