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Changeset 16222

10/09/18 16:49:05 (5 years ago)


  • added comments about parameter identification for differential equation models
  • added source code of cvodes library (part of sundials) which provides functionality to calculate gradients for the parameters of partial differential equation models efficiently using the 'adjoint state method'.
  • added compiled version of cvodes
73 added
1 edited


  • branches/2925_AutoDiffForDynamicalModels/HeuristicLab.Problems.DynamicalSystemsModelling/3.3/Problem.cs

    r16215 r16222  
    610610    #region interpretation
     612    // the following uses auto-diff to calculate the gradient w.r.t. the parameters forward in time.
     613    // this is basically the method described in Gronwall T. Note on the derivatives with respect to a parameter of the solutions of a system of differential equations. Ann. Math. 1919;20:292–296.
     615    // a comparison of three potential calculation methods for the gradient is given in:
     616    // Sengupta, B., Friston, K. J., & Penny, W. D. (2014). Efficient gradient computation for dynamical models. Neuroimage, 98(100), 521–527.
     617    // "Our comparison establishes that the adjoint method is computationally more efficient for numerical estimation of parametric gradients
     618    // for state-space models — both linear and non-linear, as in the case of a dynamical causal model (DCM)"
     620    // for a solver with the necessary features see:
     621    /*
     622     * SUNDIALS: SUite of Nonlinear and DIfferential/ALgebraic Equation Solvers
     623     * CVODES
     624     * CVODES is a solver for stiff and nonstiff ODE systems (initial value problem) given in explicit
     625     * form y’ = f(t,y,p) with sensitivity analysis capabilities (both forward and adjoint modes). CVODES
     626     * is a superset of CVODE and hence all options available to CVODE (with the exception of the FCVODE
     627     * interface module) are also available for CVODES. Both integration methods (Adams-Moulton and BDF)
     628     * and the corresponding nonlinear iteration methods, as well as all linear solver and preconditioner
     629     * modules, are available for the integration of the original ODEs, the sensitivity systems, or the
     630     * adjoint system. Depending on the number of model parameters and the number of functional outputs,
     631     * one of two sensitivity methods is more appropriate. The forward sensitivity analysis (FSA) method
     632     * is mostly suitable when the gradients of many outputs (for example the entire solution vector) with
     633     * respect to relatively few parameters are needed. In this approach, the model is differentiated with
     634     * respect to each parameter in turn to yield an additional system of the same size as the original
     635     * one, the result of which is the solution sensitivity. The gradient of any output function depending
     636     * on the solution can then be directly obtained from these sensitivities by applying the chain rule
     637     * of differentiation. The adjoint sensitivity analysis (ASA) method is more practical than the
     638     * forward approach when the number of parameters is large and the gradients of only few output
     639     * functionals are needed. In this approach, the solution sensitivities need not be computed
     640     * explicitly. Instead, for each output functional of interest, an additional system, adjoint to the
     641     * original one, is formed and solved. The solution of the adjoint system can then be used to evaluate
     642     * the gradient of the output functional with respect to any set of model parameters. The FSA module
     643     * in CVODES implements a simultaneous corrector method as well as two flavors of staggered corrector
     644     * methods–for the case when sensitivity right hand sides are generated all at once or separated for
     645     * each model parameter. The ASA module provides the infrastructure required for the backward
     646     * integration in time of systems of differential equations dependent on the solution of the original
     647     * ODEs. It employs a checkpointing scheme for efficient interpolation of forward solutions during the
     648     * backward integration.
     649     */
    611650    private static IEnumerable<Tuple<double, Vector>[]> Integrate(
    612651      ISymbolicExpressionTree[] trees, IDataset dataset, string[] inputVariables, string[] targetVariables, string[] latentVariables, IEnumerable<IntRange> episodes,
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