Calibrating Models to Data

Calibrating models to data, or finding parameters which make a model a sufficiently close fit to data, is part of the core functionality of JuliaSimModelOptimizer. The calibrate function is designed as an automated mechanism for performing such model calibration in an accurate and efficient manner. It uses alternative methods for building the cost functions, also called transcription methods, such as multiple shooting and collocation to improve the stability of the training process, along with mixing in techniques for sampling initial conditions, to ensure that an accurate fit can be generate with little to no effort.

Note

calibrate is designed for finding a single best fitting parameter set for a model. For a set of potential fits to characterize fitting uncertainty, see the documentation on the parametric_uq function for parametric uncertainty quantification

Objective function definition

JuliaSimModelOptimizer.objectiveFunction
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., b=>3.])

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
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JuliaSimModelOptimizer.cost_contributionFunction
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
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The calibrate Function

JuliaSimModelOptimizer.calibrateFunction
calibrate(prob, alg; adtype = Optimization.AutoForwardDiff())

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

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.
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Ipopt Optimizer

JuliaSimModelOptimizer.IpoptOptimizerFunction
IpoptOptimizer(;
    verbose = false,
    tol = 1e-4,
    max_iter = 1000,
    acceptable_iter = 250,
    acceptable_tol = 1e-3,
    hessian_approximation = "limited-memory", kwargs...)

A convenience constructor to create an optimizer = Ipopt.Optimizer() and set options. Other options can be passed as kwargs. See https://coin-or.github.io/Ipopt/OPTIONS.html for information about each option. The defaults provided here are more relaxed than Ipopt defaults.

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Calibration Algorithms

Single Shooting

JuliaSimModelOptimizer.SingleShootingType
SingleShooting(;maxiters = nothing, maxtime = nothing, optimizer = IpoptOptimizer(; max_iter = maxiters), 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 Ipopt.Optimizer which can be used from OptimizationMOI. We also have a convenience constructor IpoptOptimizer for defining it. The maximum number of iterations for the optimizer is given by maxiters, which is a required keyword argument.

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Multiple Shooting

JuliaSimModelOptimizer.MultipleShootingType
MultipleShooting(;maxiters = nothing, maxtime = nothing, optimizer = IpoptOptimizer(; max_iter = maxiters), interval_saveat_points = 0, trajectories = 0, 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 Ipopt.Optimizer which can be used from OptimizationMOI. We also have a convenience constructor IpoptOptimizer for defining it. The maximum number of iterations for the optimizer is given by maxiters, which is a required keyword argument.

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Initialization Methods

JuliaSimModelOptimizer.DataInitializationType
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.
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Data Shooting

JuliaSimModelOptimizer.DataShootingType
DataShooting(;maxiters = nothing, maxtime = nothing, optimizer = IpoptOptimizer(; max_iter = maxiters), groupsize, 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, 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 Ipopt.Optimizer which can be used from OptimizationMOI. We also have a convenience constructor IpoptOptimizer for defining it. The maximum number of iterations for the optimizer is given by maxiters, which is a required keyword argument.

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Collocation methods

Collocation methods work by computing the derivatives of the data instead of integrating the equations. For more details on how they work, take a look at the collocation manual page.

Note

Collocation methods work with data by design, so they can only be used when you have data in the loss functions.

Model transformations

Prediction Error Method

JuliaSimModelOptimizer.PredictionErrorMethodType
PredictionErrorMethod(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. 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.

The Prediction Error Method (PEM) is useful for calibrating unstable systems, as pure simulation methods cannot track the true solution over longer time intervals and diverge.

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