2026-07-22T08:26:20Z https://drops.dagstuhl.de/oai

oai:drops-oai.dagstuhl.de:9344 2024-03-06T10:44:06Z ddc:004 open_access

FUTURES-AMR: Towards an Adaptive Mesh Refinement Framework for Geosimulations Shashidharan, Ashwin Vatsavai, Ranga Raju Van Berkel, Derek B. Meentemeyer, Ross K. Adaptive mesh refinement Geosimulation Distributed system Multi-resolution Urban geography Adaptive Mesh Refinement (AMR) is a computational technique used to reduce the amount of computation and memory required in scientific simulations. Geosimulations are scientific simulations using geographic data, routinely used to predict outcomes of urbanization in urban studies. However, the lack of support for AMR techniques with geosimulations limits exploring prediction outcomes at multiple resolutions. In this paper, we propose an adaptive mesh refinement framework FUTURES-AMR, based on static user-defined policies to enable multi-resolution geosimulations. We develop a prototype for the cellular automaton based urban growth simulation FUTURES by exploiting static and dynamic mesh refinement techniques in conjunction with the Patch Growing Algorithm (PGA). While, the static refinement technique supports a statically defined fixed resolution mesh simulation at a location, the dynamic refinement technique supports dynamically refining the resolution based on simulation outcomes at runtime. Further, we develop two approaches - asynchronous AMR and synchronous AMR, suitable for parallel execution in a distributed computing environment with varying support for solution integration of the multi-resolution results. Finally, using the FUTURES-AMR framework with different policies in an urban study, we demonstrate reduced execution time, and low memory overhead for a multi-resolution simulation. Schloss Dagstuhl – Leibniz-Zentrum für Informatik Ashwin Shashidharan and Ranga Raju Vatsavai and Derek B. Van Berkel and Ross K. Meentemeyer 2018 Is Part Of LIPIcs, Volume 114, 10th International Conference on Geographic Information Science (GIScience 2018) InProceedings Text doc-type:ResearchArticle publishedVersion application/pdf doi:10.4230/LIPIcs.GISCIENCE.2018.16 urn:nbn:de:0030-drops-93440 https://drops.dagstuhl.de/entities/document/10.4230/LIPIcs.GISCIENCE.2018.16 eng https://creativecommons.org/licenses/by/3.0/legalcode