Overview

Develop Goals

The objectives depend on the uses of the modeling project:

• Understanding - Design a mathematical abstraction of the real world that helps to identify gaps in our mechanistic understanding of processes.
• Prediction - Design a mathematical abstraction of the real world that will be used to predict the response of the system to changes in inputs.
• Engineering - Design a mathematical abstraction of the real world with the goal of producing certain outputs given certain inputs.

Conceptualization

Complexity vs Simplicity

Formulation

• Mechanism vs. Empiricism
• Alternative Formulations (stochastic?)

Implementation

An iterative process to assemble, build and manage the tools will be used to build the model and carry out the subsequent steps: Parameterization, Model Testing, and Model Analysis.

• Code, Compile, Test, Commit, Deploy

Parameterization

• Literature-based
• Empirically based
• Calibration (Tuning)
• Role in Scaling

Model Testing

• Test model vs. development data (verification)
• Test model vs. independent data (validation)
• Test model vs. new observations (extrapolation)

Model Analysis

• Parameter Sensitivity and Uncertainty
• Formulation Uncertainty
• Conceptualization Uncertainty

Pre and Post Processing

Steps that must be done before and after running the model when the model is being used in any of the parameterizing, testing or analysis steps.

• Preparing input data
• Preparing parameter data
• Setting up model run
• Refining outputs (summarizing, visualizing, etc)