Per Grove Thomsen

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A general procedure for the construction of interpolants for Runge-Kutta (RK) formulas is presented. As illustrations, this approach is used to develop interpolants for three explicit RK formulas, including those employed in the well-known subroutines RKF45 and DVERK. A typical result is that no extra function evaluations are required to obtain an(More)
Variable-stepsize variable-formula methods (VSVFMs) are often used in the numerical integration of systems of ordinary differential equations. In this way, roughly speaking, one attempts to minimize the number of steps, Le., to select the largest possible stepsize according to a prescribed error tolerance. Very often, however, the selectmn of the stepsize(More)
A new algorithm for numerical sensitivity analysis of ordinary differential equations (ODEs) is presented. The underlying ODE solver belongs to the Runge–Kutta family. The algorithm calculates sensitivities with respect to problem parameters and initial conditions, exploiting the special structure of the sensitivity equations. A key feature is the reuse of(More)
This paper concerns predictive stepsize control applied to high order methods for temporal discretization in reservoir simulation. The family of Runge-Kutta methods is presented and in particular the explicit singly diagonally implicit Runge-Kutta (ESDIRK) methods are described. A predictive stepsize adjustment rule based on error estimates and convergence(More)
A simple numerical model for the Necturus gallbladder epithelium is presented. K+, Na+ and Cl- cross the mucosal and serosal membranes as well as the junctions by means of electrodiffusion; furthermore the mucosal membrane contains a neutral entry mechanism for NaCl and the serosal membrane contains an active pump for K+ and Na+. The values which have been(More)
Dynamic optimization by multiple shooting requires integration and sensitivity calculation. A new semi-implicit Runge-Kutta algorithm for numerical sensitivity calculation of index-1 DAE systems is presented. The algorithm calculates sensitivities with respect to problem parameters and initial conditions, exploiting the special structure of the sensitivity(More)