Provide data for as many variables as you want to calibrate:

Calibrate a parameter value:

Simulate with the calibrated parameter and compare with the data:

Use data with units to calibrate a model of a hybrid motor:

Calibrate the values of the resistance and the damping:

Use the time-value pairs of a time series as data to calibrate a model:

Calibrate the values of two parameters:

Use data generated with an input signal to calibrate a DiscreteInputOutputModel:

Specify the input signal to calibrate a parameter value:

Compute the single prediction bands and plot them together with the data and the calibrated simulation response:

Use data generated with an input signal to calibrate a TransferFunctionModel:

Specify the input signal to calibrate a parameter value:

Compute the single prediction bands and plot them together with the data and the calibrated simulation response:

Use data generated with an input signal to calibrate a StateSpaceModel:

Specify the input signal to calibrate parameter values:

Compute the single prediction bands and plot them together with the data and the calibrated simulation response:

Use data of the output of an AffineStateSpaceModel to calibrate the model:

Calibrate a parameter value:

Compute the single prediction bands and plot them together with the data and the calibrated simulation response:

Indicate initial guess values for the calibration parameters:

Calibrate two parameter values:

Introduce constraints for the calibration parameters:

Calibrate two parameter values:

Confidence intervals are computed assuming an unconstrained model:

Specify a model name for the calibrated model:

Calibrate the value of the damping parameter, specifying the calibrated model name:

Create a calibrated model that extends the input model:

Use data generated with an input signal to calibrate a model:

Specify the input signal to calibrate parameter values:

Specify a fixed value for a parameter to calibrate another:

Specify initial values and a simulation interval to calibrate a model:

Calibrate a parameter value:

Provide weights for the calibration of several variables:

Calibrate a parameter value:

Compute the single prediction bands and plot them together with the data and the calibrated simulation response:

Find the list of available properties:

These can be extracted more conveniently from the CalibratedSystemModel:

Extract the single prediction bands plot:

Extract the single prediction bands plot with a custom confidence level:

Extract the single prediction bands plot with a custom variance estimator function:

Extract the calibrated model and the calibrated parameters:

Extract the single prediction bands:

Extract the single prediction bands evaluated at time t:

Extract the root mean squared error for each calibration variable:

Extract the root mean squared error for each calibration variable providing a validation set:

Extract parameter confidence information, including estimates, standard errors, confidence intervals,

-statistics for parameter estimates

, and ‐values for parameter ‐statistics:

Use data of the states of a model to calibrate a parameter:

Use the default confidence level:

Specify a custom confidence level of 0.5:

Use data of an output to calibrate a model:

Use a loss function with no regularization to calibrate parameters:

Use a Lasso regularization to calibrate parameters:

Use a Tikhonov regularization to calibrate parameters:

Use data of the angular velocity of a component to calibrate a model of a hybrid motor:

Use the default simulation method to calibrate the value of the damping parameter:

Calibrate, specifying a number of interpolation points for simulation:

Use data of an output to calibrate a model:

Use the default fitting method to calibrate parameters:

Use the conjugate gradient method to calibrate parameters:

Use data of an output to calibrate a model:

Use the default norm in the loss function to calibrate parameters:

Use the -norm in the loss function to calibrate parameters:

Use the 1-norm in the loss function to calibrate parameters

Use data of the angular velocity of a component to calibrate a model of a hybrid motor:

Calibrate the values of the resistance and the damping with the default progress reporting:

Calibrate without progress reporting:

Use data of an output to calibrate a model:

Calibrate parameters using no validation data:

Calibrate parameters by reserving a fraction of the calibration data for validation:

Use a particular random seed in the selection of validation data to ensure predictable results:

Use a custom validation set to calibrate parameters:

Use data of the states of a model to calibrate a parameter:

Use the default estimate of error variance:

Estimate the variance by the mean absolute error:

Use temperature data to estimate the heat flow rate for a burner in a model of a heating system:

Compute the single prediction bands and plot them together with the data and the calibrated simulation response:

Plot the residuals against time:

Recreate the behavior of a speaker by calibrating a simpler circuit using simulation results as input data:

Compare the behavior of both models before calibration:

Retrieve simulation results as data and use them for calibration:

Compare the calibrated model with the speaker:

Calibrate the viscous contribution to friction in a model of a body held by a spring on top of an accelerating conveyor belt:

The total kinetic friction can be modeled as a combination of viscous, Coulomb and Stribeck components:

The equations of motion must include the event-generating effect of the static friction, which holds the body static with respect to the belt until an upper bound is breached by external forces:

Set initial values for the position and velocity of the body and the discrete variable tracking the impact of static friction:

Set parameter values and create the system model:

Calibrate the contribution of the viscous term with data for the position of the body:

Find the 95% confidence interval for the viscous friction factor:

Compute the single prediction bands and plot them together with the data and the calibrated simulation response:

Compute the extremes of the position and velocity for the calibrated simulation data:

Deduce the controller parameters in a controlled system for which you have step response data:

Connect the plant model to a controller with symbolic parameters:

Calibrate the parameters in the model with the step response data of the controlled system:

Compare the simulation results of the calibrated model with the reverse-engineered model:

Estimate the mass of a planet that orbits a star from the trajectory of one of its moons in a reduced three-body planetary system:

The dynamics of the moon are dictated by Newtonian gravity:

Set parameter values for the system and initial values for the trajectory of the moon:

Create a model from the equations, parameters and initial values:

Simulate the model before calibration and plot the trajectories of the three bodies:

Calibrate a model with data for the trajectory of the moon:

Retrieve the simulation data and plot the trajectories for the calibrated model:

Compare the data with the calibrated trajectory: