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
- 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.
MADS documentation, including description of all modules, functions, and variables, is available at:
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"))
import Mads; Mads.test()
import Pkg; Pkg.test("Mads")
To explore getting-started instructions, execute:
import Mads; Mads.help()
Various examples located in the
examples directory of the
A list of all the examples is provided by:
A specific can be executed using:
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:
include(joinpath(Mads.dir, "examples", "bigdt", "bigdt.jl"))
To explore evailable notebooks, execute:
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