**MADS** is distributed with a series of example files that can be used to test and verify **MADS** performance. The following executions can be performed after the code has been successfully installed.

- Optimization: Parameter Estimation (
**PE**), Calibration, Inversion - Uncertainty Quantification (UQ)
- Sensitivity analysis (SA)

mads a01 test=3 opt=squads igrnd real=1

No input files are needed for this **MADS** execution. In this case, **a01** is a problem root name for **MADS** output files. **MADS** solves optimization problem based on 2-dimensional Rosenbrock test function (2 parameters, 2 observations) using random initial guesses for model parameters. The analysis is performed using the **Squads** optimization technique.

mads a01 test=3 igrnd real=1

**MADS** solves optimization problem based on 2-dimensional Rosenbrock test function (2 parameters, 2 observations) using a single set of random initial guesses for model parameters. The analysis is performed using the Levenberg-Marquardt (**LM**) optimization technique (**LM** is the default optimization technique).

mads a01 test=3 igrnd real=1000 truth=0.1

**MADS** solves optimization problem based on 2-dimensional Rosenbrock test function (10 parameters, 10 observations) using 1000 different random initial guesses for model parameters. The analysis is performed using the Levenberg-Marquardt (**LM**) optimization technique. The optimization is terminated if the estimated model parameters are with absolute error less than 0.1 from their true values (in this case at the global minimum, all the model parameters are equal to 1)

mads a01 test=3 dim=10 igrnd real=10 retry=1000 truth=0.1

**MADS** solves optimization problem based on 10-dimensional Rosenbrock test function (10 parameters, 10 observations) using 10 different random initial guesses for model parameters. The analysis is performed using the Multi-Start Levenberg-Marquardt (**MSLM**) optimization technique with up to 1000 retries. The optimization is terminated if the estimated model parameters are with absolute error less than 0.1 from their true values (at the global minimum, all the model parameters are equal to 1)

mads example/contamination/s01 ldebug

**MADS** control file **s01.mads** is located in directory **example/contamination**. **MADS** solves a source identification optimization problem using the Levenberg-Marquardt (**LM**) optimization technique.

mads example/contamination/s02 ldebug

**MADS** control file **s02.mads** is located in directory **example/contamination**. **MADS** solves a source identification optimization problem using the Levenberg-Marquardt (**LM**) optimization technique.

mads example/contamination/s01 ldebug igrnd real=1

**MADS** solves a source identification optimization problem using the Levenberg-Marquardt (**LM**) optimization technique. The analysis is performed using a single set of random initial guesses for model parameters.

mads example/contamination/s01 seed=1549170842 obsrange igrnd real=1

**MADS** solves a source identification optimization problem using the Levenberg-Marquardt (**LM**) optimization technique. The analysis is performed using random initial guesses for model parameters based on a user-defined seed. The analysis is terminated when model predictions are within predefined calibration ranges defined in the **MADS** control file **s01.mads** (keyword '**obsrange**').

mads example/contamination/s01 opt=squads seed=1549170842 eigen obsrange pdebug igrnd real=1

**MADS** solves a source identification optimization problem using the **Squads** optimization technique. Eigen analysis of the obtained final solution os performed after the optimization has been terminated (keyword '**eigen**').

mads example/contamination/s01 opt=pso seed=1549170842 eigen obsrange igrnd real=1

**MADS** solves a source identification optimization problem using the **PSO** optimization technique.

mads w01 np=2 ldebug pardebug=2

**MADS** identifies aquifer parameters based on pumping test data using the Levenberg-Marquardt optimization technique. All the files associated with this problem are located in directory example/wells. The analysis is performed in parallel using 2 processors.

For this example, the code **WELLS** is needed, and it can be obtained at wells.lanl.gov

mads example/contamination/s02 monte real=100

**MADS** performs uncertainty quantification (**UQ**) using Monte Carlo analysis based on 100 forward runs with random model parameters. Random model parameters are generated using Improved Distributed Hypercube Sampling (IDHS) (default technique when number of realizations is less than 500). **MADS** control file s02.mads is located in directory example/contamination.

mads example/contamination/s02 abagus

**MADS** performs global uncertainty and sensitivity analysis using **ABAGUS**.

mads example/contamination/s02 gsens

**MADS** performs global sensitivity analysis (**SA**) of model parameters. The analysis is uses Sobol's technique.

mads example/contamination/s02 lsens

**MADS** performs local sensitivity analysis (**SA**) for the current model parameters (as provided in the **MADS** control file s02.mads). The analysis is based on local Jacobian matrix.

mads example/contamination/s02 opt=squads lsens

**MADS** performs global optimization using the **Squads** optimization technique. After the optimization is complete, **MADS** performs local sensitivity analysis for the optimized model parameters. The analysis is based on a local Jacobian matrix.