Reducing the Uncertainty When Approximating the Solution of ODEs

Abstract : One can reduce the uncertainty in the quality of an approximate solution of an ordinary differential equation (ODE) by implementing methods which have a more rigorous error control strategy and which deliver an approximate solution that is much more likely to satisfy the expectations of the user. We have developed such a class of ODE methods as well as a collection of software tools that will deliver a piecewise polynomial as the approximate solution and facilitate the investigation of various aspects of the problem that are often of as much interest as the approximate solution itself. We will introduce measures that can be used to quantify the reliability of an approximate solution and discuss how one can implement methods that, at some extra cost, can produce very reliable approximate solutions and therefore significantly reduce the uncertainty in the computed results.
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Andrew M. Dienstfrey; Ronald F. Boisvert. 10th Working Conference on Uncertainty Quantification in Scientific Computing (WoCoUQ), Aug 2011, Boulder, CO, United States. Springer, IFIP Advances in Information and Communication Technology, AICT-377, pp.280-293, 2012, Uncertainty Quantification in Scientific Computing. 〈10.1007/978-3-642-32677-6_18〉
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Wayne Enright. Reducing the Uncertainty When Approximating the Solution of ODEs. Andrew M. Dienstfrey; Ronald F. Boisvert. 10th Working Conference on Uncertainty Quantification in Scientific Computing (WoCoUQ), Aug 2011, Boulder, CO, United States. Springer, IFIP Advances in Information and Communication Technology, AICT-377, pp.280-293, 2012, Uncertainty Quantification in Scientific Computing. 〈10.1007/978-3-642-32677-6_18〉. 〈hal-01518671〉

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