2003
DOI: 10.1088/0264-9381/21/2/019
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Towards standard testbeds for numerical relativity

Abstract: In recent years, many different numerical evolution schemes for Einstein's equations have been proposed to address stability and accuracy problems that have plagued the numerical relativity community for decades. Some of these approaches have been tested on different spacetimes, and conclusions have been drawn based on these tests. However, differences in results originate from many sources, including not only formulations of the equations, but also gauges, boundary conditions, numerical methods, and so on. We… Show more

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Cited by 99 publications
(178 citation statements)
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“…Since one aspect of the test suite put forward in [21] is that different numerical implementations of one and the same evolution system can be compared, let us point out that our results for ADM and BSSN agree with [21] for the specific results shown there (see also [31]). …”
Section: Discussionsupporting
confidence: 66%
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“…Since one aspect of the test suite put forward in [21] is that different numerical implementations of one and the same evolution system can be compared, let us point out that our results for ADM and BSSN agree with [21] for the specific results shown there (see also [31]). …”
Section: Discussionsupporting
confidence: 66%
“…Hence our initial data differ slightly form the ones in [21], who add random noise to all quantities which need initialization.…”
Section: A Robust Stability Testmentioning
confidence: 99%
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“…We also hope that these results and these well-defined test cases can serve as a basis for future code validations and comparisons much in the spirit of Ref. [12].…”
Section: Discussionmentioning
confidence: 87%
“…A strong test for stability involves injecting (pseudo) random noise into the evolution, via the initial and boundary data [35,36]. By this method, any exponentially growing error modes, if present, will be stimulated at a much higher amplitude than would naturally occur due to truncation or round-off error.…”
Section: Pseudo Random Noisementioning
confidence: 99%