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MADS (Model Analysis & Decision Support)

MADS is an integrated open-source high-performance computational (HPC) framework in Julia for data analytics and model diagnostics. MADS can execute a wide range of data- and model-based analyses:

  • Sensitivity Analysis
  • Parameter Estimation
  • Model Inversion and Calibration
  • Uncertainty Quantification
  • Model Selection and Model Averaging
  • Model Reduction and Surrogate Modeling
  • Machine Learning (e.g. Blind Source Separation, Source Identification, Feature Extraction, Matrix / Tensor Factorization, etc.)
  • Decision Analysis and Support

MADS has been tested to perform HPC simulations on a wide-range multi-processor clusters and parallel environments (Moab, Slurm, etc.). MADS utilizes adaptive rules and techniques which allows the analyses to be performed with minimum user input. The code provides a series of alternative algorithms to execute each type of data- and model-based analyses.

For additional information:

  • web:
  • documentation:
  • repos:
  • git:
    • git clone (recommended)
    • git clone (might not be up-to-date)
  • docker:
    • docker run --interactive --tty montyvesselinov/madsjulia
  • email:

Builds & Tests

Mads Build & Test Status @ Travis Continuous Integration (CI) service (OS X & linux)

Travis Status

Coverage of the Build-in Mads Tests

Coverage Status

Publications, Presentations, Projects