PEST++ is a software suite aimed at supporting complex numerical models in the decision-support context. Much focus has been devoted to supporting environmental models (groundwater, surface water, etc) but these tools are readily applicable to any computer model.

Master branch:

Develop branch:

The latest official report documenting PEST++ is available from the USGS:

https://pubs.er.usgs.gov/publication/tm7C26

White, J.T., Hunt, R.J., Fienen, M.N., and Doherty, J.E., 2020, Approaches to Highly Parameterized Inversion: PEST++ Version 5, a Software Suite for Parameter Estimation, Uncertainty Analysis, Management Optimization and Sensitivity Analysis: U.S. Geological Survey Techniques and Methods 7C26, 52 p., https://doi.org/10.3133/tm7C26.

The lastest PEST++ users manual is available here or as a word document.

As of version 4.3.11, PEST++ pre-compiled binaries for windows and linux are available as a github release:

https://github.com/usgs/pestpp/releases

The develop branch includes a Visual Studio solution, as well as CMake files for building on all operating systems using g++, MSVC, and/or intel C++.

See details here to compile using CMake.

The PEST++ software suite includes several stand-alone tools for model-independent (non-intrusive) computer model parameter estimation and uncertainty analysis. Codes include:

• pestpp-glm: deterministic GLM parameter estimation using "on-the-fly" subspace reparameterization, effectively reproducing the SVD-Assist methodology of PEST without any user intervention and FOSM-based parameter and (optional) forecast uncertainty estimation with support for generating Bayes-linear posterior parameter realizations.

• pestpp-sen: Global sensitivity analysis using either Morris or Sobol

• pestpp-swp: a generic parallel run utility driven by a CSV file of parameter values

• pestpp-opt: chance-constrained linear programming

• pestpp-ies: iterative ensemble smoother implementation of GLM (based on the work Chen and Oliver 2013) with support for generic localization (local analysis and/or covariance localization)

• pestpp-mou: multi-objective optimization under uncertainty using evolutionary algorithms (single objective also!)

• pestpp-da: model-independent ensemble-based sequential and batch iterative data assimilation with options to use standard Kalman update, multiple data assimilation (MDA), or the GLM algorithm of Chen and Oliver (2013).

All members of the software suite can be compiled for PC, MAC, or Linux and have several run managers to support parallelization. Windows users with older OS versions should use the iwin binaries (starting "i", compiled with intel C++) to avoid the dreaded MSVC missing runtime DLL issue.

Funding for PEST++ has been provided by the U.S. Geologial Survey. The New Zealand Strategic Science Investment Fund as part of GNS Science’s (https://www.gns.cri.nz/) Groundwater Research Programme has also funded contributions 2018-present. Intera, Inc. also provides ongoing support for PEST++.

White, J.T., Hunt, R.J., Fienen, M.N., and Doherty, J.E., 2020, Approaches to Highly Parameterized Inversion: PEST++ Version 5, a Software Suite for Parameter Estimation, Uncertainty Analysis, Management Optimization and Sensitivity Analysis: U.S. Geological Survey Techniques and Methods 7C26, 52 p., https://doi.org/10.3133/tm7C26.

White, J. T., 2018, A model-independent iterative ensemble smoother for efficient history-matching and uncertainty quantification in very high dimensions. Environmental Modelling & Software. 109. 10.1016/j.envsoft.2018.06.009. http://dx.doi.org/10.1016/j.envsoft.2018.06.009.

White, J. T., Fienen, M. N., Barlow, P. M., and Welter, D.E., 2017, A tool for efficient, model-independent management optimization under uncertainty. Environmental Modeling and Software. http://dx.doi.org/10.1016/j.envsoft.2017.11.019.

Welter, D.E., White, J.T., Hunt, R.J., and Doherty, J.E., 2015, Approaches in highly parameterized inversion— PEST++ Version 3, a Parameter ESTimation and uncertainty analysis software suite optimized for large environmental models: U.S. Geological Survey Techniques and Methods, book 7, chap. C12, 54 p., http://dx.doi.org/10.3133/tm7C12.

Welter, D.E., Doherty, J.E., Hunt, R.J., Muffels, C.T., Tonkin, M.J., and Schreüder, W.A., 2012, Approaches in highly parameterized inversion—PEST++, a Parameter ESTimation code optimized for large environmental models: U.S. Geological Survey Techniques and Methods, book 7, section C5, 47 p., available only at http://pubs.usgs.gov/tm/tm7c5.

• https://www.usgs.gov/software/pest-parameter-estimation-code-optimized-large-environmental-models
• http://www.pesthomepage.org
• https://github.com/pypest/pyemu

The benchmarks folder contains a simple worked example and basic testing routines that are used for basic CI testing. Many full-worked test problems of varying sizes are now located in separate repos:

• pestpp-glm benchmarks
• pestpp-ies benchmarks
• pestpp-opt benchmarks
• pestpp-mou benchmarks
• pestpp-da benchmarks

Much work has been done to avoid additional external dependencies in PEST++. As currently designed, the project is fully self-contained.

please see the PEST++ users manual in the documentation directory for a current and complete description of all ++ options

This software has been approved for release by the U.S. Geological Survey (USGS). Although the software has been subjected to rigorous review, the USGS reserves the right to update the software as needed pursuant to further analysis and review. No warranty, expressed or implied, is made by the USGS or the U.S. Government as to the functionality of the software and related material nor shall the fact of release constitute any such warranty. Furthermore, the software is released on condition that neither the USGS nor the U.S. Government shall be held liable for any damages resulting from its authorized or unauthorized use