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.
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- ReadtheDocs (outdated)
- LANL (outdated).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.
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"))Execute:
import Mads; Mads.test()or
import Pkg; Pkg.test("Mads")To explore getting-started instructions, execute:
import Mads; Mads.help()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"))To explore available notebooks, please execute:
Mads.notebooks()docker run --interactive --tty montyvesselinov/madsjulia