Last call! - spaces are still available for two parameter estimation and
uncertainty analysis course to be held in the UK and USA this summer.
Best wishes,
Matt Tonkin, SSP&A
matt@...
Courses: Model Calibration and Predictive Uncertainty using PEST
Shrewsbury, England, September 13-15, 2005
Stony Brook, Long Island, NY, September 19-21, 2005 (near MacArthur Airport
and Port Jefferson)
Principal Instructor: Dr. John Doherty (author of PEST)
Guest Instructor: Jim Rumbaugh (author of Groundwater Vistas) (New
York course only)
Assistant Instructor: Matt Tonkin
This intensive course instructs participants on the automated calibration of
environmental models, and the analysis of the predictive uncertainty
associated with such models. The principal instructor is the developer of
PEST, the industry standard package for model-independent, automated
calibration and predictive uncertainty analysis. The course focuses on the
use of PEST, including the new capabilities added to Versions 8 and 9 this
year. The course includes thorough coverage of the theory and application of
nonlinear parameter estimation techniques in the calibration of different
types of models. There is a strong practical aspect of the course -
participants gain hands-on experience in the use of PEST, including its
advanced regularization and predictive analysis features, in the calibration
of groundwater flow and transport models, surface water quality and quantity
models, as well as other types of models including a vadose zone model and a
biological growth model.
Course Details and Registration
For detailed information on course content visit
www.sspa.com/pest/training.html to download a prospectus, or write to
pest@....
To register on-line for the New York course please visit
www.sspa.com/pest/training.html
To register on-line for the UK course please visit
http://www.esinternational.com/training/CourseDetail.asp?course_id=49
Information about PEST can be found at pest@.... This account was
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updates. The account is used to circulate occasional information. To be
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