MADS (Model Analysis & Decision Support)

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
• Risk Assessment
• Decision Analysis and Support

MADS utilizes adaptive rules and techniques that 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 descriptions of all modules, functions, and variables, is available at:

• GitHub (always up-to-date)
• ReadtheDocs (outdated)
• LANL (outdated).

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

Detailed demontrative data analysis and model diagnostics problems are available 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 are 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 available notebooks, please execute:

Mads.notebooks()

Docker

docker run --interactive --tty montyvesselinov/madsjulia

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

Publications, Presentations, Projects

• mads @ GitLab
• mads @ GitHub
• mads @ LANL
• SmartTensors.com
• SmartTensors @ GitHub
• SmartTensors @ LANL
• monty @ GitLab
• monty @ GitHub
• monty @ LANL