MADS.md

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

• mads.gitlab.io
• madsjulia.github.io
• mads.lanl.gov
• monty.gitlab.io
• ees.lanl.gov/monty

Related Julia Packages

• SmartTensors: Unsupervised and Physics-Informed Machine Learning based on Matrix/Tensor Factorization
• RegAE: Regularization with a variational autoencoder for inverse analysis
• Geostatistical Inversion with randomized + sketching optimization

Additional information:

• web:
  • mads.lanl.gov (Julia version of MADS)
  • madsc.lanl.gov (C version of MADS)
• documentation:
  • github (recommended)
  • readthedocs
  • madsjulia.lanl.gov (might not be up-to-date)
• repos:
  • github (recommended)
  • gitlab (might not be up-to-date)
• git:
  • git clone [email protected]:madsjulia/Mads.jl (recommended)
  • git clone [email protected]:mads/Mads.jl (might not be up-to-date)
• docker:
  • docker run --interactive --tty montyvesselinov/madsjulia
• email: [email protected]