Uncertainty propagation or box propagation

Abstract This paper discusses the use of recently developed techniques and software in the numerical propagation of uncertainties in initial coordinates and/or parameters for initial value problems. We present an approach based on several validated numerical integration techniques but focusing on the propagation of boxes. The procedure uses a multivariable high order Taylor series development of the solution of the system whose Taylor coefficients are calculated via extended automatic differentiation rules for all the basic operations. These techniques are implemented in the recent free-software TIDES . The classical two-body and Lorenz problems are chosen as examples to show the benefits of the approach. The results show that the solution of uncertainties can be approximated in an analytic form by means of a Taylor series and that these techniques can be extremely useful in different practical applications.

[1]  Andreas Griewank,et al.  Introduction to Automatic Differentiation , 2003 .

[2]  Roberto Barrio,et al.  VSVO formulation of the taylor method for the numerical solution of ODEs , 2005 .

[3]  George F. Corliss,et al.  Solving Ordinary Differential Equations Using Taylor Series , 1982, TOMS.

[4]  Roberto Barrio,et al.  A three-parametric study of the Lorenz model , 2007 .

[5]  Nedialko S. Nedialkov,et al.  Validated solutions of initial value problems for ordinary differential equations , 1999, Appl. Math. Comput..

[6]  D. Wilczak,et al.  Cr-Lohner algorithm , 2011 .

[7]  Angel Jorba,et al.  A Software Package for the Numerical Integration of ODEs by Means of High-Order Taylor Methods , 2005, Exp. Math..

[8]  M. Berz,et al.  TAYLOR MODELS AND OTHER VALIDATED FUNCTIONAL INCLUSION METHODS , 2003 .

[9]  Roberto Barrio,et al.  Breaking the limits: The Taylor series method , 2011, Appl. Math. Comput..

[10]  M. Stadtherr,et al.  Validated solutions of initial value problems for parametric ODEs , 2007 .

[11]  Andreas Griewank,et al.  Automatic Differentiation of Algorithms: From Simulation to Optimization , 2000, Springer New York.

[12]  E. Lorenz Deterministic nonperiodic flow , 1963 .

[13]  Piotr Zgliczynski,et al.  C1 Lohner Algorithm , 2002, Found. Comput. Math..

[14]  Roberto Barrio,et al.  Performance of the Taylor series method for ODEs/DAEs , 2005, Appl. Math. Comput..

[15]  Roberto Barrio,et al.  Sensitivity Analysis of ODES/DAES Using the Taylor Series Method , 2005, SIAM J. Sci. Comput..

[16]  Roberto Barrio,et al.  Bounds for the chaotic region in the Lorenz model , 2009 .

[17]  Nicholas J. Higham,et al.  INVERSE PROBLEMS NEWSLETTER , 1991 .

[18]  Vincent Lefèvre,et al.  MPFR: A multiple-precision binary floating-point library with correct rounding , 2007, TOMS.

[19]  R. Park,et al.  Nonlinear Mapping of Gaussian Statistics: Theory and Applications to Spacecraft Trajectory Design , 2006 .

[20]  Martin Berz,et al.  COSY INFINITY Version 9 , 2006 .

[21]  Michèle Lavagna,et al.  Application of high order expansions of two-point boundary value problems to astrodynamics , 2008 .

[22]  Roberto Barrio,et al.  Algorithm 924: TIDES, a Taylor Series Integrator for Differential EquationS , 2012, TOMS.

[23]  Stephen Wolfram,et al.  The Mathematica Book , 1996 .

[24]  Martin Berz,et al.  Verified Integration of ODEs and Flows Using Differential Algebraic Methods on High-Order Taylor Models , 1998, Reliab. Comput..

[25]  G. Corliss,et al.  ATOMFT: solving ODEs and DAEs using Taylor series , 1994 .