GENIE is a model-independent suite of programs that can be used to generally distribute, manage, and execute multiple model runs via the TCP/IP infrastructure. The suite consists of a file distribution interface, a run manage, a run executer, and a routine that can be compiled as part of a program and used to exchange model runs with the run manager.

• What Is Parameter Estimation
• Model Parameter Estimation
• Parameter Estimation Example

Aug 31, 2016  pyEMU is a set of python modules for model-independent, user-friendly, computer model uncertainty analysis. PyEMU is tightly coupled to the open-source suite PEST (Doherty 2010a and 2010b, and Doherty and other, 2010) and PEST (Welter and others, 2015, Welter and other, 2012), which are tools for model-independent parameter estimation. Parameter Estimation (PEST) The Parameter Estimation (PEST) software is a model-independent nonlinear parameter estimation and optimization tool developed by Watermark Numerical Computing (Doherty, 2010). The purpose of PEST is to assist in data interpretation, model calibration and predictive analysis (Doherty, 2010). Parameter Estimation (PEST) software. PEST is a model-independent parameter optimization program that mini-mizes one or more user-specified objective functions. PEST implements a particularly robust variant of the Gauss-Marquardt-Levenberg Marquardt, 1963 method of non-linear parameter estimation. While this method requires that. GENIE is a model-independent suite of programs that can be used to generally distribute, manage, and execute multiple model runs via the TCP/IP infrastructure. The suite consists of a file distribution interface, a run manage, a run executer, and a. These are SENSAN, a model-independent sensitivity analyser, and PARREP, a utility that facilitates the commencement of a new PEST run using parameter values generated on a previous PEST run. However by far the most important changes to PEST are the improved capabilities that it offers for user intervention in the parameter estimation process.

GENIE is a model-independent suite of programs that can be used to generally distribute, manage, and execute multiple model runs via a TCP/IP network. The suite consists of a file distribution interface, a run manager, a run executer, and a routine that can be compiled as part of a program and used to exchange model runs with the run manager. Because communication is via a standard protocol (TCP/IP), any computer connected to the Internet can serve in any of the capabilities offered by this suite. Model independence is consistent with the existing template and instruction file protocols of the widely used PEST parameter estimation suite.

VERISON HISTORY

Version 2 (CURRENT) - September 18, 2015

Version 1 - April 18, 2012

GENIE FAQs

GENIE Version 2 Functionality
What does GENIE do?
GENIE is designed to allow parallel access to networked computing resources. It can be used to generally distribute, manage, and execute multiple model runs via TCP/IP networks. This functionality is currently available for PPEST (Doherty, 2010) and PEST++ (Welter and others, 2015). In addition to the run manager and run executer that are of most use to the majority of users, the full suite also includes a GENIE_INTERFACE routine that can be compiled as part of any program and used to exchange model runs with the run manager. Because communication is via TCP/IP, any computer connected to the Internet can serve in any of the capacities offered by this suite. Model independence is consistent with the existing template and instruction file protocols of the widely used PEST program. The www.pesthomepage.org description of GENIE can be found here.

Can GENIE make my non-parallel code run in parallel across a network?
Perhaps yes, if the serial runs are 'embarrassingly parallel' (numerical modeling problems characterized by multiple independent calculations with little communication needed between the independent calculations). The GENIE_INTERFACE provides a conduit for information regarding the exchange of model runs and associated results with the GENIE manager for an outside or calling program. The outside program needs to prepare the model run information in the array format required by GENIE and then needs to call the GENIE_INTERFACE routine. This interface routine allows dynamic two-way communication between the outside program and the GENIE manager. The routine is written in C++ but contains the necessary external interface for compilation with Fortran. Version 2 of the GENIE_INTERFACE is described in Muffels and others (2015), Appendix 2 of the PEST++ Version 3 report.

What are some of the limitations of GENIE?
The GENIE design is intended to extensible. Limitations of version 2 include:

• Unlike BeoPEST, which includes MPI and TCP/IP communication, GENIE version 2 supports only TCP/IP communication.
• Only IP v4 is fully tested; IP v6 is available in version 2 but only cursorily tested.

Common Issues with GENIE
Will GENIE remotely copy and launch slaves on distributed resources?
No, GENIE manages the communication between a non-parallel computing code and the distributed computing resources. The user must manually copy the run directory and launch GENIE. The ability to remotely copy the run directory and launch GENIE is provided by companion software PESTCommander (Karanovic and others, 2012).

Why doesn't the GENIE Master see the GENIE Executer?
GENIE communications are handled through the widely used TCP/IP protocol that is standard for Internet communications. Firewall and computer security policies commonly close TCP/IP ports. Although GENIE can use any of the standard TCP/IP ports (ports between 1024 and 65535), the port chosen by the user will need to be unblocked by the wide-area/local area networks, and the Windows operating systems running GENIE components. Moreover, the same port number needs to be input to both the GENIE master and associated GENIE slaves.

Why are there a minimum of 3 DOS command windows needed to run GENIE rather than 2 DOS command windows needed by BeoPEST and PPEST?
Because GENIE is a general run manager that can be applied to a variety of non-parallel computing codes; one DOS command window is needed to run the original non-parallel computing code (the outside or calling program), one is needed to run the GENIE in Master mode, and one or more are needed to run the GENIE in executer (or client) mode. PPEST and BeoPEST are specialized and non-general parallel computing codes, thus have GENIE Master-type capabilities integrated within the original code.

Non-USGS GENIE Reports, Articles, and Related Background Material

(See Publications tab above for official USGS publications)

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Dahlstrom, D.J., and Carter, J.T.V., 2013, Inverse Modeling with PEST++ and GENIE. Groundwater 51(2): 162-167. doi:10.1111/gwat.12021

Doherty, J., 2014a, PEST, Model-independent parameter estimation-user manual (5th ed., with slight additions): Brisbane, Australia, Watermark Numerical Computing.

Doherty, J., 2014b, Addendum to the PEST manual: Brisbane, Australia, Watermark Numerical Computing.

Doherty, J., 2015, PEST - The Book: Calibration and Uncertainty Analysis for Complex Environmental Models. Watermark Numerical Computing, Brisbane, Australia, ISBN: 978-0-9943786-0-6, 227 p.

Muffels, C.T., Schreüder, W.A., Doherty, J., Karanovic, M., Tonkin, M.J., Hunt, R.J., and Welter, D.E., 2011, A model independent TCP/IP run manager, in MODFLOW and More 2011: Integrated Hydrologic Modeling, Proceedings of the 10th International Conference of the International Ground Water Modeling Center. Golden, CO: Colorado School of Mines.

Schreüder, W.A., Muffels, C.T., Tonkin, M.J., Doherty, J., Hunt, R.J., and Welter, D.E., 2011, Efficient use of parallel resources using PEST, in MODFLOW and More 2011: Integrated Hydrologic Modeling, Proceedings of the 10th International Conference of the International Ground Water Modeling Center. Golden, CO: Colorado School of Mines.

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Visual MODFLOW Flex provides a seamless interface to the popular parameter estimation and predictive analysis program PEST, developed by Dr. John Doherty of Watermark Computing. This section provides instructions on using Visual MODFLOW Flex to setup, run, and interpret a Parameter Estimation/Predictive Analysis simulation. In addition, this chapter provides a brief description of the input parameters and settings required by PEST. A detailed description of the algorithms, parameters, input files, and other options for PEST are available in the PEST User Documentation. This can be accessed from www.PESTHomepage.org

Visual MODFLOW supports both the Calibration and Predictive Analysis capabilities of the PEST program, and it allows you to run parameter estimation using results from both groundwater flow and contaminant transport simulations (i.e. observations can consist of heads, concentrations, and groundwater flux). Support for transport, will be added in future releases.

Acknowledgements:

Excerpts from the following publications are used throughout this documentation:

•Using Pilot Points to Calibrate a MODFLOW/MT3D Model, Doherty (2008)

•PEST: Model-Independent Parameter Estimation, User Manual: 7th Edition, Doherty (2019)

•Addendum to the PEST Manual, June 2012

•www.PESTHomepage.org

Suggested References:

Several USGS publications are also available on PEST. See:

Approaches to Highly Parameterized Inversion: a Guide to Using PEST for Groundwater Model Calibration:

Approaches to Highly Parameterized Inversion: a Guide to Using PEST for Model Parameter and Predictive Uncertainty Analysis'

Approaches to Highly Parameterized Inversion: Pilot Point Theory, Guidelines and Research Directions

Loading the PEST Workflow

What Is Parameter Estimation

The PEST workflow can be launched from the 'Select Run Type' step in a numerical workflow, as shown below; simply click on the PEST button in the main window.

Before attempting to run a parameter estimation simulation, make sure your model meets the following requirements:

Model Parameter Estimation

•The model runs successfully (converges) and produces meaningful results. The model should provide a solution under a variety of input parameter conditions. Parameter estimation is as much an art as it is a science, and therefore, it should only be used to complement your own efforts in understanding the system.

•The model has one or more (preferably many more) observations against which to compare the calculated results. Observations can be in the form of measured or estimated values of head or concentration at discrete points in the model, or in the form of measured or estimated groundwater fluxes into (or out of) one or more grid cells.

•It is also recommended that you do a sensitivity analysis prior to a full-fledged PEST run. In a sensitivity analysis, parameter values are individually changed to determine the effect on model calibration and prediction. The results give an indication of which parameter changes can have significant impact on the model results (these are sensitive parameters) and which parameter changes have little or no impact on the model results (these are non-sensitive or insensitive parameters). This should help you to property select parameters to include in a PEST run, as you should focus more on the sensitive parameters, and less on the non-sensitive parameters.

•The observation times (for heads) lie within the start/stop time of the numerical simulation. If you have imported a model from Visual MODFLOW Classic, ensure that an appropriate start date was defined in Visual MODFLOW Classic before loading the project into Visual MODFLOW Flex. If you have generated the numerical model from a conceptual model workflow, ensure that the start date at the modeling objectives is appropriate based on the head observation times you have defined.

Setting up a PEST Run

PEST requires several inputs and a number of steps that must be completed in a specific order. Fortunately, Visual MODFLOW Flex provides the PEST GUI in a workflow, that guides you through the sequential steps and necessary inputs, running PEST, and analyzing the results:

•Define Observations and Assign Weights

•Define Parameters (Property Zones, Boundary Conditions)

•Define Pilot Points

•Define Kriging Variograms

•Select Regularization (None, Tikhonov, SVD Assist)

•Define PEST Run Settings and Run PEST

•Run Sensitivity Analysis

•Analyze Results

•Update Model Inputs

Parameter Estimation Example

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