Courses in model calibration, model uncertainty analysis, and the application of the program PEST are presented by the author of PEST, Dr. John Doherty, throughout the world.  Three-day courses have a strong practical aspect, where participants are guided through exercises designed to illustrate important facets of model calibration, experimental design, and predictive analysis.  At these courses, participants are also encouraged to bring their own models to investigate the use of PEST.  One-day workshops are occasionally presented that serve to introduce attendees to the theory and application of inverse modeling.

Upcoming PEST Courses

Introductory and Advanced PEST Courses: Portland, Oregon  May 10-13, 2010

General    

This will be the second “Double Header” PEST course, comprising a two-day “Introduction to PEST” course and a subsequent two-day “Advanced Analysis using PEST” course.

The introductory course will be most suitable to modelers who have little to no experience with inverse methods. This course will comprise a balance of about 60:40 lectures and hands-on-exercises, with the aim of providing theoretical background on inverse methods based on least-squares, and practical experience in their implementation in conjunction with a range of environmental models. This course will include many examples from the groundwater modeling context, together with other environmental modeling applications.

In the advanced course the emphasis will turn to highly parameterized inversion in calibration of ground and surface water models, and in exploration of the uncertainty associated with the parameters and predictions associated with these models. Topics covered in detail will include the use of pilot points as a parameterization device; advanced regularization techniques; the “SVD-assist” technique for efficient inversion of highly parameterized models; linear and nonlinear analysis of uncertainty - including the recently-developed and highly expedient null-space Monte Carlo methodology that is unique to PEST; and methods for optimizing future data acquisition to reduce uncertainty. This course will comprise a balance of about 90% lectures and 10% hands-on exercises. However, there will be ample room for discussion.

A complete course description and registration is available here