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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 multi-processor clusters, parallel and cloud computing environments (including Moab, Slurm, etc.).

MADS utilizes adaptive rules and techniques that allow the analyses to be performed with minimum user input.

MADS provides a series of alternative algorithms to execute various types of data- and model-based analyses implemented in the code.

Publications, Presentations, Projects

Additional information:

  • web:
  • documentation:
  • repos:
  • git:
    • git clone git@github.com:madsjulia/Mads.jl (recommended)
    • git clone git@gitlab.com:mads/Mads.jl (might not be up-to-date)
  • docker:
    • docker run --interactive --tty montyvesselinov/madsjulia
  • email: mads@lanl.gov

LA-CC-15-080