Popularity
94 Stars
Updated Last
11 Months Ago
Started In
January 2016

MADS (Model Analysis & Decision Support)

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MADS is an integrated high-performance computational framework for data/model/decision analyses.

MADS can be applied to perform:

  • Sensitivity Analysis
  • Parameter Estimation
  • Model Inversion and Calibration
  • Uncertainty Quantification
  • Model Selection and Averaging
  • Model Reduction and Surrogate Modeling
  • Decision Analysis and Support

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

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

MADS can efficiently utilize available computational resources.

MADS has been extensively tested and verified.

Documentation

MADS documentation, including description of all modules, functions, and variables, is available at:

MADS information is also available at mads.gitlab.io and madsjulia.github.io

Detailed demontrative data ananlysis and model diagnostics problems are availble as Julia scripts and Jupyter notebooks. See also below.

Installation

In Julia REPL, execute:

import Pkg; Pkg.add("Mads")

To utilize the latest code updates use:

import Pkg; Pkg.add(Pkg.PackageSpec(name="Mads", rev="master"))

Testing

Execute:

import Mads; Mads.test()

or

import Pkg; Pkg.test("Mads")

Getting started

To explore getting-started instructions, execute:

import Mads; Mads.help()

Examples

Various examples located in the examples directory of the Mads repository.

A list of all the examples is provided by:

Mads.examples()

A specific can be executed using:

Mads.examples("contamination")

or

include(joinpath(Mads.dir, "examples", "contamination", "contamination.jl"))

This example will demonstrate various analyses related to groundwater contaminant transport.

To perform Bayesian Information Gap Decision Theory (BIG-DT) analysis, execute:

Mads.examples("bigdt")

or

include(joinpath(Mads.dir, "examples", "bigdt", "bigdt.jl"))

Notebooks

To explore evailable notebooks, execute:

Mads.notebooks()

Docker

docker run --interactive --tty montyvesselinov/madsjulia

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