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MADS (Model Analysis & Decision Support)
MADS is an integrated open-source high-performance computational (HPC) framework in Julia. 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, 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:
- mads.lanl.gov
- madsc.lanl.gov (C version of MADS)
- documentation:
- github (recommended)
- readthedocs
- madsjulia.lanl.gov (it might not be up-to-date)
- repos:
- git:
git clone git@github.com:madsjulia/Mads.jl(recommended)git clone git@gitlab.com:mads/Mads.jl(it might not be up-to-date)
- email: mads@lanl.gov
Builds & Tests
Mads Build & Test Status @ JuliaLang.org
Mads Build & Test Status @ Travis Continuous Integration (CI) service (OS X & linux)
Coverage of the Build-in Mads Tests
LA-CC-15-080