Calibrating Models to Data

objective(prob, alg)

When considering the InverseProblem in the context of calibration, we can define an objective function that associates a number to a vector of possible search space values. This number is a measure of how close is the given vector to a solution of the inverse problem. The objective for an inverse problem is defined as a sum of the individual contributions from each experiment.

\[\sum_{i=1}^{N} loss(i).\]

In the above loss(i) is the individual contribution of the ith experiment and this can be computed via cost_contribution and is determined by the loss_func passed in the corresponding experiment constructor.

Calling objective(prob, alg) will return a function of the search space values, so in order to use it, it is best to store it in a variable as follows:

cost = objective(prob, alg);

In order to obtain a particular value that corresponds to some search space values, one has to input a NamedTuple or a vector of pairs to the above mentioned function.

As an example, one would call

cost([a => 1.0, b => 3.0])

where the search space in the inverse problem was defined as containing the a and b parameters, i.e. [a => (0.0, 10.0), b => (-5.0, 5.0)].

The above function also accepts calibration results as input, so if we have a calibration result obtained from something like

r = calibrate(prob, alg)

then we can compute the cost value corresponding to the calibration result using cost(r), where cost is the above mentioned variable.

The cost function returned by objective can also be called with 0 arguments, i.e. cost(), in which case the cost will be computed using the initial state of the optimization problem, which uses the default values present in the model for all parameters and initial conditions.

Positional arguments

• prob: the InverseProblem defining the objective
• alg: the calibration algorithm that formulates the objective function

cost_contribution(alg, experiment::AbstractExperiment, prob, x)

Compute the contribution of the given experiment to the total cost associated with the inverse problem prob using the given calibration algorithm (alg).

Positional arguments

• alg: the calibration algorithm that formulates the objective function.
• experiment: the experiment for which we compute the contribution.
• prob: the InverseProblem defining the objective.
• x: the values of the tuned parameters or initial conditions.

calibrate(prob, alg; adtype = AutoForwardDiff(), progress = true)

Find the best parameters to solve the inverse problem prob using the calibration algorithm given by alg.

Arguments

• prob: the InverseProblem to solve.
• alg: the calibration algorithm used for building the loss function. This can be JuliaHubJob for launching batch jobs in JuliaHub. In this case, the actual alg is wrapped inside JuliaHubJob object.

Keyword Arguments

• adtype: Automatic differentiation choice, see the Optimization.jl docs for details. Defaults to AutoForwardDiff().
• progress: Show the progress of the optimization (current loss value) in a progress bar. Defaults to true.

calibrate(res, alg; adtype = AutoForwardDiff(), progress = true)

Continue calibration from res which is obtained from a previous calibration using the algorithm given by alg.

Arguments

• res: object of type CalibrationResult which is obtained from a previous calibration of an InverseProblem.
• alg: the calibration algorithm used for building the loss function. This can be JuliaHubJob for launching batch jobs in JuliaHub. In this case, the actual alg is wrapped inside JuliaHubJob object.

Keyword Arguments

• adtype: Automatic differentiation choice, see the Optimization.jl docs for details. Defaults to AutoForwardDiff().
• progress: Show the progress of the optimization (current loss value) in a progress bar. Defaults to true.

SingleShooting(;maxiters = nothing, maxtime = nothing, optimizer = IpoptOptimizer(hessian_approximation = "limited-memory"), kwargs...)

Single shooting is the simplest transcription method for building the loss function. In this case we solve all the model equation and then we compare the solutions against the data with the user given error functions.

The optimization method will get the cost function as the sum of the individual previously mentioned costs. To change the optimization method, you can use the optimizer keyword argument. The default optimization method is IpoptOptimizer(hessian_approximation = "limited-memory") which can be used from OptimizationIpopt. The maximum number of iterations for the optimizer is given by maxiters, which is a required keyword argument. Atleast one of maxiters or maxtime is required. Optimization stops when either one of the condition is met. Atleast one of maxiters or maxtime is required. If both of them are given, optimization stops after either of condition is first met.

Keyword Arguments

• maxiters: Maximum numbers of iterations when using the method with a time stepping optimizer.
• maxtime: Maximum time for the optimization process.
• optimizer: An Optimization.jl algorithm for the global optimization. Defaults to IpoptOptimizer(hessian_approximation = "limited-memory").
• kwargs: Keyword arguments passed onto Optimization.solve.

MultipleShooting(;maxiters = nothing, maxtime = nothing, optimizer = IpoptOptimizer(), trajectories, ensemblealg = EnsembleThreads(), continuitytype = ModelStatesPenalty, continuitylossfun = squaredl2loss, continuitylossweight = 100, initialization = DefaultSimulationInitialization(), kwargs...)

Multiple shooting is a calibration algorithm in which we split the solves of the model equations in several parts, as it can be easier to avoid local minima if we fit the individual segments and them match them up. The number of groups can be controlled via the trajectories keyword argument. The initial conditions between the segments are added to the optimization as extra hidden parameters that have to be fit. The continuity of the resulting solution ensemble is enforced using a continuity penalty between segments.

The continuity loss function can be changed via continuitylossfun, defaulting to squaredl2loss and the penalty used for the continuity is weighted via a the continuitylossweight, which defaults to 100. All segments can be initialized using initialization keyword argument. It defaults to DefaultSimulationInitialization(), i.e. the values are obtained by simulating the model passed in the Experiment with default parameters. For information on other initializations, refer Initialization Methods section in the manual.

The optimization method will get the cost function as the sum of the individual previously mentioned costs. To change the optimization method, you can use the optimizer keyword argument. The default optimization method is IpoptOptimizer which can be used from OptimizationIpopt. The maximum number of iterations for the optimizer is given by maxiters, which is a required keyword argument. Atleast one of maxiters or maxtime is required. If both of them are given, optimization stops after either of condition is first met.

Keyword Arguments

• maxiters: Maximum numbers of iterations when using the method with a time stepping optimizer.
• maxtime: Maximum time for the optimization process.
• optimizer: An Optimization.jl algorithm for the global optimization. Defaults to IpoptOptimizer which can be used from OptimizationIpopt.
• trajectories: Required. This is number of segments that the whole time span is split into.
• ensemblealg: Parallelization method for the ensemble problem. Defaults to EnsembleThreads().
• continuitytype: Type of continuity enforced. This can be ModelStatesPenalty which adds a term in the loss which corresponds to continuity loss given by continuitylossfun or it can be ConstraintBased where the continuity equations are added as constraints for the optimizer.
• continuitylossfun: Loss function to compute continuity penalty. This is only applicable if continuitytype is ModelStatesPenalty.
• continuitylossweight: Weight multiplied with the continuity loss term in the total loss. This is only applicable if continuitytype is ModelStatesPenalty.
• initialization: Initialization method of the segments. This is only applicable if continuitytype is ModelStatesPenalty.
• kwargs: Keyword arguments passed onto Optimization.solve.

DefaultSimulationInitialization()

This is used for initializing segments for doing calibration using MultipleShooting. The values for all the segments are computed by simulating the model passed in the Experiment with default parameters and using its ODESolution.

DataInitialization(; interpolation = CubicSpline)

This is used for initializing segments for doing calibration using MultipleShooting. It is used for those states where data is present and the values are obtained by fitting using interpolation. For other states, initial condition for that state is used for all the segments.

Keyword Arguments

• interpolation: Interpolation method from DataInterpolations which can be passed to fit data for obtaining initial values for the segments.

RandomInitialization()

This is used for initializing segments for doing calibration using MultipleShooting. The values for all the segments are computed as a random value from 0 to 1 multiplied by the initial condition for that state.

DataShooting(;maxiters = nothing, maxtime = nothing, optimizer = IpoptOptimizer(), groupsize, ensemblealg = EnsembleThreads(), continuitylossfun = squaredl2loss, continuitylossweight = 100, kwargs...)

DataShooting is a variant of the multiple shooting method (see MultipleShooting for more details), where we use the data for the internal initial conditions between segments instead of having the optimizer find them. If the data is considered noisy, it might be better to let the optimizer find the initial conditions, as we would inevitably introduce errors if we use DataShooting. To specify the group size, which is the number of time points in each trajectory where data points on both ends are taken as initial conditions, we can use groupsize keyword argument. The continuity of the resulting solution ensemble is enforced using a continuity penalty between segments. The continuity losss function can be changed via continuitylossfun, defaulting to squaredl2loss and the penalty used for the continuity is weighted via a the continuitylossweight, which defaults to 100.

The optimization method will get the cost function as the sum of the individual previously mentioned costs. To change the optimization method, you can use the optimizer keyword argument. The default optimization method is IpoptOptimizer which can be used from OptimizationIpopt. The maximum number of iterations for the optimizer is given by maxiters, which is a required keyword argument. Atleast one of maxiters or maxtime is required. If both of them are given, optimization stops after either of condition is first met.

Keyword Arguments

• maxiters: Maximum numbers of iterations when using the method with a time stepping optimizer.
• maxtime: Maximum time for the optimization process.
• optimizer: An Optimization.jl algorithm for the global optimization. Defaults to Ipopt.Optimizer.
• groupsize: Required. Number of time points in each trajectory.
• ensemblealg: Parallelization method for the ensemble problem. Defaults to EnsembleThreads().
• continuitytype: Type of continuity enforced. This can be ModelStatesPenalty which adds a term in the loss which corresponds to continuity loss given by continuitylossfun or it can be ConstraintBased where the continuity equations are added as constraints for the optimizer.
• continuitylossfun: Loss function to compute continuity penalty. This is only applicable if continuitytype is ModelStatesPenalty.
• continuitylossweight: Weight multiplied with the continuity loss term in the total loss. This is only applicable if continuitytype is ModelStatesPenalty.
• initialization: Initialization method of the segments. This is only applicable if continuitytype is ModelStatesPenalty.
• kwargs: Keyword arguments passed onto Optimization.solve.

DiscreteFixedGainPEM(alpha)

This model transformation implements the prediction error method as a discrete callback that brings the solution closer to the data at the corresponding timepoints as the integrator steps through with a factor that depends on alpha, which is a scalar value from 0 to 1. If alpha is 0, then this is equivalent with not applying any correction, while alpha=1 means that we start each step from the data.

If there is data for the unknowns, it is directly used. If there is data for observed variables, corresponding equations which are linear with respect to the unknowns are used to estimate other unknowns in those equations for which there is no data. This is only the case if the system of equations are invertible with respect to those other unknowns.

It is also useful for calibrating unstable systems, as pure simulation methods cannot track the true solution over longer time intervals and diverge.