Parameter Estimation

How to use the Parameter Estimation tool to automatically tune model parameters for performance

The Parameter Estimation tool is used to optimize the output of a submodel to track real world measurement data (or any other data you choose) by finding optimal initial conditions (i.e., design parameters).

Data File

Choose a data file (CSV format) from your project that contains time series data in it. Then choose a column of data that you want the output of your submodel to track. Your objective signal will be subtracted from this signal to derive the total error.


Choose a signal from your model that you want to optimize. This signal will be subtracted from the values in your data file to generate an error signal which will be summed (integrated) over the course of the simulation to arrive at a final cumulative value.


Constraints are additional signals that guide the optimization process. If a constraint signal goes positive (the value at any time step is greater than zero), then the parameter values under test are considered to be invalid. You can have multiple constraints. If you specify constraint signals, the number of algorithms available to you will be reduced, since not all algorithms support constraints.

Design Parameters

These are the model parameters that you are trying to optimize. You can add as many as you like to this list, and all of them will be varied in order to find the best values. The more you add here, the longer the optimization process may take.

For each parameter, you can specify the following values:

  • Initial: The starting value at the beginning of the optimization process. This is your best guess or a known sane value.

  • Minimum: The minimum value, if any, that the parameter can reasonably have. Leave it blank (-inf) if there is no minimum.

  • Maximum: The maximum value, if any, that the parameter can reasonably have. Leave it blank (+inf) if there is not maximum.


Please see the Optimization Algorithms page for details on specific algorithms.

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