Abstract
Previous work on multilevel techniques for compression and reduction of scientific data is extended to the case of data given on unstructured meshes in two and three dimensions. The centerpiece of the work is a decomposition algorithm which is shown to be optimal, in terms of both storage and operational complexity, applicable to unstructured grids in both two and three dimensions, and which implicitly gives a Riesz basis that can be exploited to reduce the data while maintaining rigorous bounds on the loss incurred. The flexibility of the approach is illustrated by applications to potential flow around an airfoil and the effect of compression on quantities of interest relevant to airfoil design; compression of computational simulation of a nonlinear reactiondiffusion system with special attention given to the problem of time series reduction; and, data from a simulation of magnetically confined plasma in a fusion reactor reduced so as to preserve the electric field computed from the data.
| Original language | English |
|---|---|
| Pages (from-to) | A1402-A1427 |
| Journal | SIAM Journal on Scientific Computing |
| Volume | 42 |
| Issue number | 2 |
| DOIs | |
| State | Published - 2020 |
Funding
∗Submitted to the journal’s Methods and Algorithms for Scientific Computing section June 12, 2019; accepted for publication (in revised form) January 22, 2020; published electronically April 28, 2020. This manuscript has been authored in part by UT-Battelle, LLC, under contract DE-AC05-00OR22725 with the US Department of Energy (DOE). The US government retains and the publisher, by accepting the article for publication, acknowledges that the US government retains a nonexclusive, paid-up, irrevocable, worldwide license to publish or reproduce the published form of this manuscript, or allow others to do so, for US government purposes. DOE will provide public access to these results of federally sponsored research in accordance with the DOE Public Access Plan (http://energy.gov/downloads/doe-public-access-plan). https://doi.org/10.1137/19M1267878 †Division of Applied Mathematics, Brown University, Providence, RI 02912 (Mark Ainsworth@ brown.edu, ozan [email protected], ben [email protected]).
| Funders | Funder number |
|---|---|
| U.S. Department of Energy |
Keywords
- Data reduction
- Lossy compression
- Unstructured data
