Get Started with FePEST
Beside the standard task of model calibration (or typically named parameter estimation), FePEST provides a wide range of possibility to explore situations of best and worst-case scenarios, uncertainty quantification, etc. This section is intended to draw you the road-map for the different workflows in FePEST.
How to Start: Some First Concepts
- Estimation
- Prediction
- Monte Carlo
- Pareto
Estimation
Estimation mode is applied for cases of model calibration, so-called history matching. FePEST combines several tools and options of estimation and regularization modes to get most out of PEST suit.
KEYWORDS: Observation Definitions, Parameter Definitions, Prior Information, Tikhonov regularization, Subspace regularization, Super Parameters
Prediction
When FePEST runs in Prediction mode the task is to identify the best or worst case scenarios. The definition of what best/worst is, depends strictly on the conceptual model. A worst-case scenario analysis could be the parameter estimation, which drives to the maximum concentration at a specific river section, maximum pressure on a open-cast walls, etc.
KEYWORDS: Predictive Analysis
Monte Carlo
FePEST can run the entire Monte Carlo workflow with several levels of flexibility. Either the analysis is evaluated with a not necesary calibrated model, so-called Pre-Calibration Monte Carlo Analysis. Or the model has been already calibrated and a Post-Calibration Monte Carlo Analysis can take full advantage of the regularization knowledge (e.g. reduced number of problem dimensions).
KEYWORDS: Stochastic Parameter Generation , Null-Space Projection
Pareto
Sometimes a specific observation set (or groups) influences strongly the estimation (i.e. calibration) of parameter sets. FePEST provides the newly-implemented Pareto operation mode. Here you can understand the contribution of this specific observation group to total measurement objective function, e.g. what would it happen if the calibrated parameters if this groups is more /less calibrated.
KEYWORDS: Pareto, Observation Definitions
Standard Workflow
Out-of-the-box Workflows
Drawing sensitivity maps
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Run PEST under Estimation mode and compute the Jacobian matrix only (Termination criteria) in Problem Settings dialog.
Running a sensitivity analysis
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For a more detailed exploration of the uncertainty of parameter definitions to the model predictions (observations), it is strongly recommended to use workflow Calibrated-constrained Monte Carlo Analysis |
