PEST98 - Model-Independent Parameter Estimation

New Features in PEST98
Introduction to PEST
Parallel PEST
Other PEST Features
PEST Utilities for MODFLOW/MT3D
PEST Utilities for GMS
PEST Requirements

What's New About PEST98?

PEST98 is the latest version of PEST. It includes Parallel PEST and SENSAN as well as several utilities for automatic generation and checking of PEST input files.

New Features In PEST98

Introduction to PEST

PEST is revolutionizing model calibration. In hydrology, engineering, geophysics, biology, economics and many other fields, PEST is changing the way people work with models. Nonlinear parameter estimation has never been this easy or this robust. For the first time, PEST places this powerful mathematical technique into the hands of all modelers.
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Matching model-generated data to field data becomes easy with PEST.

PEST is a nonlinear parameter estimation package with a difference! The difference is that PEST can be used to estimate parameters for just about any existing model whether or not you have the model's source code. PEST is able to "take control" of a model running it as many times as it needs while adjusting its parameters until the discrepancies between selected model outputs and a complementary set of field or laboratory measurements is reduced to a minimum in the weighted least-squares sense.

Most parameter estimation packages have two serious drawbacks that inhibit their ability to optimize parameters for the plethora of computer simulation models that are used today in all fields of study. The first of these difficulties is that a model normally needs to be partially recoded in order to communicate with an estimation program. This usually involves recasting the model as a subroutine which is then called by the estimator each time it needs to run the model. The second disadvantage is that the performance of many commercial and public-domain estimators is seriously degraded when optimizing parameters for large numerical models or for the sometimes complex models used for simulating "messy" environmental processes.

PEST overcomes the first of these difficulties by communicating with a model through the model's own input and output files. Thus PEST adapts to the model; the model does not need to be adapted to PEST. It overcomes the second problem by implementing a particularly robust variant of the Gauss-Marquardt-Levenberg method of nonlinear parameter estimation. Furthermore, through adjustment of a number of control variables, a user is able to "tune" PEST's implementation of the method to suit the model for which parameters are sought.
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Complexity is no hindrance to PEST.

Because PEST is model-independent, the "model" can, in fact, be a series of models which PEST runs in succession through a batch file. PEST can estimate parameters for one or all of the models simultaneously. Thus a first model can provide input data for a second model; a single model can be calibrated against a number of different historical datasets at one time; a preprocessor can be run, followed by the model, followed by a postprocessor - the possibilities are endless. The only requirements for the "model" are that it can be run from the command line and that it reads and writes ASCII files.

If a computer model is being used to understand or interpret data pertaining to a natural or man-made system, the chances are that the model's performance will be significantly enhanced through the use of PEST to parameterize that model. PEST has been used successfully in most scientific fields including groundwater and surface-water hydrology, geophysics, geomechanics, chemical, aeronautical and mechanical engineering, biology, and soil science, among others.

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SENSAN for Model-Independent Sensitivity Analysis

The latest version of PC PEST includes SENSAN, a model-independent sensitivity analyzer. Like PEST, SENSAN communicates with a model through the model's own input and output files. Thus SENSAN can be used with a model without the user having to make any alterations to the model.

Prior to running SENSAN, a modeler simply identifies the adjustable parameters on model input file(s) and model-generated numbers on model output file(s) that are of particular interest. The user then provides SENSAN with different sets of parameter values, there being no upper limit to the number of sets thus provided. SENSAN runs the model for each set, recording parameter values and model outputs in spreadsheet format for easy later analysis. Relative model output variations as well as sensitivities of model outputs to parameter changes are also recorded.

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Parallel PEST for Calibrating Large and Complex Models

Parallel PEST distributes model runs across networked PCs. Where model run times are large and adjustable parameters are many, the saving in overall PEST optimization time through the use of Parallel PEST is enormous. Thus Parallel PEST can be used in the calibration of large and complex models where application of nonlinear parameter estimation techniques would have previously been considered impossible.

Implementation of the Gauss-Marquardt-Levenberg parameter estimation algorithm used by PEST requires that derivatives of model outcomes be calculated with respect to adjustable parameters. By carrying out these model runs simultaneously, each optimization iteration can be completed in a fraction of the time that would have been required had all model runs been carried out in succession on a single machine.
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By undertaking simultaneous model runs on different machines on a PC network, enormous savings can be made in PEST optimization time.

Other PEST Features