Conference Summary
Conference Program
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Conference Abstracts
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Pre-Conference Short Course
Data Assimilation and Optimization: Concepts and Basic Math
Instructors: Jeremy White, John Doherty, Randy Hunt, Mike Fienen
Do not fear the maths. The concepts are more important than the equations; this course will discuss the concepts which the equations illuminate.
These concepts will help you to understand what happens under the hood of PEST and PEST++. But they go much further than this. They will help you understand what can be expected from decision-support modeling in general, and how the decision support modeling process can be tailored to achieve these expectations.
Discussions will not be limited to singular value decomposition. They will extend to how you can decompose an environmental problem in a way that allows modeling to address that problem. This knowledge can support the design of a sleek and efficient modeling process that can harvest information from data, direct it to the making of an important decision, and clarify the risks associated with that decision. In so doing, you can incorporate all that modeling can offer into the decision-making process.
Post-Conference Short Course
Introduction of Workflow Automation with Python and PEST++
Instructors: Mike Fienen, Randy Hunt, Jeremy White
The confluence of rapid, robust, and repeatable environmental modeling is a special unicorn that needs taming. The PEST++ framework brings the robust tools needed for pushing environmental models to their decision-making potential. Repeatability and, with some unicorn taming, increased efficiency is achieved through open-source workflow tools built around the PEST++ framework. In this short course, we use a worked example model to provide an example predictive-modeling workflow from connecting a model to PEST, through analysis and data assimilation, to decision-making endpoints, leveraging scripting throughout the process. The workflow tools are in the python programming language.
