Fundamental Problem Setup
The fundamental setup prepares the model to be processed by PEST. Its steps are common to all basic and advanced PEST methods. Before you start with the fundamental setup, we recommend you to take a look on the section Is the FEFLOW model ready? to check some preliminary steps.

Creating a new FePEST problem
Open a new project and choose the related FEFLOW model 
Parameters
A list of adjustable parameters including their locations (zones of parameter constancy or continuous parameter distributions interpolated from pilot points) defines which parameters are to be estimated. 
Observations
Historical data that act as the calibration targets is supplied. This choice will constitute the measurement objective function, whose reduction is one of the primary targets of model calibration. 
Prior Knowledge
A calibration solution based on the minimization of the objective function alone is nonunique in almost all cases of environmental modelling. Expert knowledge (Prior Knowledge) of the expected geological situations lowers the degree of nonuniqueness by preferring parameter distributions that are considered to be plausible by geological expertise. 
Regularization
Even after the inclusion of rich observation data and constraints of expert knowledge, a common calibration problem can still show significant nonuniqueness. This can introduce numerical instability to the inversion process. Tikhonov regularization, subspace regularization and regularization by Super Parameters (SVDAssist) are techniques quite useful in these situations. Especially subspace methods and regularization by Super Parameters can separate parameter space into combinations of parameters which are estimable on the basis of the current calibration dataset and combinations of parameters which are inestimable, and hence define the null space. Estimation of only estimable parameter combinations ensures numerical stability of the fitting process. It also allows calculation of some informative statistics related to parameter identifiability and parameter/predictive uncertainty. In addition, if using SVDAssist, significant reductions of optimization runtime can be achieved as PEST only needs to undertake as many model runs per iteration as the number of uniquely estimable combinations of parameters. 
Parallel Computing
Distributing the computational load of the undertaking many model runs to support the inversion process over a number of different machines reduces the overall processing time. Parallelization is a requirement for almost any highlyparameterized model to meet usual project time frames.