Estimating Computational Noise in Numerical Simulations

Jorge J. Moré and Stefan M. Wild

  eigf_noise.png

warning This page is heavily under construction, proceed with caution warning

This page contains supplemental information for the papers:

  1. Estimating Computational Noise by J. Moré and S. Wild.
    SIAM J. Scientific Computing, 33(3):1292-1314, 2011. DOI: 10.1137/100786125
    [PDF from SIAM], [PDF from earlier preprint]
  2. Estimating Derivatives of Noisy Simulations by J. Moré and S. Wild.
    ACM Transactions on Mathematical Software, 38(3):19:1-19:21, 2012. DOI: 10.1145/2168773.2168777
    [PDF from ACM], [PDF from earlier preprint]
  3. Do You Trust Derivatives or Differences? by J. Moré and S. Wild.
    J. Computational Physics, 273:268-277, 2014. DOI: 10.1016/j.jcp.2014.04.056
    [PDF from JCP], [PDF from earlier preprint]

    Supplementary material page for 7 tested solvers.

Application problems, comments, questions, and suggestions should be addressed to Stefan Wild at mcs .

Estimating Computational Noise

The following information (used in [1]) is provided to encourage the estimation of computational noise in applications and to determine strengths and limitations of the code. We provide the following Matlab scripts for producing basic noise level estimates from data:

Sample Problems

The following source files can be used to define the simulation-based problems in [1]:

Data