Algorithm 658_ ODESSA - An ordinary differential equation solver with explicit simultaneous sensitivity analysis. ODESSA is a package of FORTRAN routines for simultaneous solution of ordinary differential equations and the associated first-order parametric sensitivity equations, yielding the ODE solution vector below;(t) and the first-order sensitivity coefficients with respect to equation parameters below;. ODESSA is a modification of the widely disseminated initial-value solver LSODE, and retains many of the same operational features. Standard program usage and optional capabilities, installation, and verification considerations are addressed herein. (Source: http://dl.acm.org/)

This software is also peer reviewed by journal TOMS.

References in zbMATH (referenced in 28 articles , 1 standard article )

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  1. Perić, Nikola D.; Villanueva, Mario E.; Chachuat, Benoît: Sensitivity analysis of uncertain dynamic systems using set-valued integration (2017)
  2. Sabet, Sahand; Poursina, Mohammad: Computed torque control of fully-actuated nondeterministic multibody systems (2017)
  3. Rodriguez-Fernandez, Maria; Banga, Julio R.; Doyle, Francis J. III: Novel global sensitivity analysis methodology accounting for the crucial role of the distribution of input parameters: application to systems biology models (2012)
  4. Sandu, Adrian; Miehe, Philipp: Forward, tangent linear, and adjoint Runge-Kutta methods for stiff chemical kinetic simulations (2010)
  5. Duran, Ahmet: Sensitivity analysis of asset flow differential equations and volatility comparison of two related variables (2009)
  6. Hooker, Giles: Forcing function diagnostics for nonlinear dynamics (2009)
  7. Duran, Ahmet; Caginalp, Gunduz: Parameter optimization for differential equations in asset price forecasting (2008)
  8. Perry, M. A.; Atherton, M. A.; Bates, R. A.; Wynn, H. P.: Bond graph based sensitivity and uncertainty analysis modelling for micro-scale multiphysics robust engineering design (2008)
  9. Dehaan, Darryl; Guay, Martin: A new real-time method for nonlinear model predictive control (2007)
  10. Miehe, Philipp; Sandu, Adrian: Forward, tangent linear, and adjoint Runge-Kutta methods in KPP-2.2 (2006)
  11. Baker, C. T. H.; Bocharov, G. A.: Computational aspects of time-lag models of Marchuk type that arise in immunology (2005)
  12. Rico-Martínez, R.; Gear, C. W.; Kevrekidis, Ioannis G.: Coarse projective kMC integration: Forward/reverse initial and boundary value problems (2004)
  13. Sulieman, H.; McLellan, P. J.; Bacon, D. W.: A profile-based approach to parametric sensitivity in multiresponse regression models (2004)
  14. Rihan, Fathalla A.: Sensitivity analysis for dynamic systems with time-lags (2003)
  15. Runborg, Olof; Theodoropoulos, Constantinos; Kevrekidis, Ioannis G.: Effective bifurcation analysis: a time-stepper-based approach (2002)
  16. González-García, R.; Rico-Martínez, R.; Wolf, W.; Lübke, M.; Eiswirth, M.; Anderson, J. S.; Kevrekidis, I. G.: Characterization of a two-parameter mixed-mode electrochemical behavior regime using neural networks (2001)
  17. Krishnan, J.; Engelborghs, K.; Bär, M.; Lust, K.; Roose, D.; Kevrekidis, I. G.: A computer-assisted study of pulse dynamics in anisotropic media (2001)
  18. Bangia, Anil K.; Batcho, Paul F.; Kevrekidis, Ioannis G.; Karniadakis, George Em.: Unsteady two-dimensional flows in complex geometries: Comparative bifurcation studies with global eigenfunction expansions (1997)
  19. Feehery, William F.; Tolsma, John E.; Barton, Paul I.: Efficient sensitivity analysis of large-scale differential-algebraic systems (1997)
  20. Maly, Timothy; Petzold, Linda R.: Numerical methods and software for sensitivity analysis of differential-algebraic systems (1996)

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