SimEnv Overview
Simulation is one of the cornerstones in scientific research. The aim of the SimEnv project is to develop a toolbox oriented simulation environment that enables the modeller to handle model related quality assurance matters (Saltelli et al., 2000 & 2004) and scenario analyses. Both research foci require complex simulation experiments for model inspection, validation and control design without changing the model in general.
SimEnv aims at model evaluation by performing simulation runs with a model in a coordinated manner and running the model several times. Coordination is achieved by predefined experiment types representing multirun simulations.
According to the strategy of a selected experiment type for a set of socalled factors x which represent parameters, initial or boundary values, or drivers of a model M a numerical sample is generated before simulation. This sample corresponds to a multirun experiment with the model. During the experiment for each single simulation run the factors x are adjusted numerically according to the sample and the factors’ default values. Each experiment results in a sequence of model outputs for selected state variables z of the model M in the space of all addressed factors {X}. Model outputs can be processed and evaluated after simulation generally on the state space and experimenttype specifically on the factor space.
The following experiment types form the base of the SimEnv multirun facility: 
Example for {X} = (x_{1},x_{2}) x = sample points o = factor's default value 
Global Sensitivity Analysis  Elementary Effects Method Qualitative ranking of a large number of factors x with respect to their sensitivity on model output at random trajectories in the factor space {X}. For determination of the most important factors. ( > = trajectories)


Deterministic Factorial Design Inspection of the model’s behaviour in the factor space {X} by a discrete numerical sampling with a flexible inspection strategy for subspaces. For model verification, numerical validation, deterministic error analysis, deterministic control design, scenario analysis and spatial patch model applications. 

Local Sensitivity Analysis Determination of model (state variable’s z) local sensitivity to factors x. Is performed by finite difference derivative approximations from M. For numerical validation purposes, model analysis, submodel sensitivity. 

Uncertainty Analysis  Monte Carlo Analysis Factor space {X} sampling by perturbations according to probability density functions. Determination of moments, confidence intervals and heuristic probability density functions for state variables in the course of experiment postprocessing. For error analysis, uncertainty analysis, verification and validation of deterministic models. (only a subsample is shown)


Global Sensitivity Analysis  Variance Based Method Orthogonal variance decomposition of model output to first order and total effects of factors by a Monte Carlo resampling experiment For decomposition of variance at model output to input factors. (+ = second sample; only subsamples are shown)


Bayesian Calibration Reduce uncertainty about factor values by deriving a representative sample from factor prior distributions while having measurement values from the system available for model – measurement comparison. For uncertainty analysis and reduction with a Bayesian technique. (Trace plot of a MCMC chain)


Optimization  Simulated Annealing Determination of optimal factor values x by a simulated annealing method for a cost functions derived from z. For model validation (system  model comparison), control design, decision making. (only a subsample is shown)

SimEnv makes use of modern IT concepts. Model preparation for interfacing them to SimEnv is based on minimal source code manipulations by implementing interface functions into Fortran, C/C++, Python and Java model source code and at shell script level for the addressed factors and for model output. Additionally, interfaces for Matlab, Mathematica and GAMS models are available.
In experiment preparation an experiment type is selected and equipped numerically by sampling the factor space. Experiment performance supports local, remote, and parallel / distributed hardware architectures to distribute work load of the single runs of the experiment.
Experiment specific model output postprocessing enables navigation in the complex factor  model output space and interactive filtering of model output and reference data by application of operator chains. SimEnv supplies builtin operators and enables specification of userdefined and composed operators.
Result evaluation is dominated by application of preformed visualization modules. SimEnv model output as well as experiment postprocessing offer data interfaces for NetCDF, IEEE compliant binary and ASCII format for a more detailed postprocessing outside SimEnv.
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