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 Model Averaging
- Model Reduction and Surrogate Modeling
- Machine Learning (e.g., Blind Source Separation, Source Identification, Feature Extraction, Matrix / Tensor Factorization, etc.)
- Decision Analysis and Support
MADS has been extensively tested and verified.
MADS can efficiently utilize available computational resources.
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 each type of data- and model-based analyses.
Documentation
Detailed documentation including description of all MADS modules and functions is available at GitHub, ReadtheDocs and LANL sites.
See also mads.gitlab.io and madsjulia.github.io
Detailed model diagnostics problem is demonstrated here
Installation
After starting Julia, execute:
import Pkg; Pkg.add("Mads")
to access the latest released version. To utilize the latest updates (commits) use:
import Pkg; Pkg.add(Pkg.PackageSpec(name="Mads", rev="master"))
Docker
docker run --interactive --tty montyvesselinov/madsjulia
Testing
import Mads; Mads.test()
Examples
To explore getting-started instructions, execute:
import Mads; Mads.help()
There are various examples located in the examples
directory of the Mads
repository.
For example, execute
include(Mads.madsdir * "/../examples/contamination/contamination.jl")
to perform various example analyses related to groundwater contaminant transport, or execute
include(Mads.madsdir * "/../examples/bigdt/bigdt.jl")
to perform Bayesian Information Gap Decision Theory (BIG-DT) analysis.
Installation of MADS behind a firewall
Julia uses git for package management.
Add in the .gitconfig
file in your home directory to support git behind a firewall:
[url "git@github.com:"]
insteadOf = https://github.com/
[url "git@gitlab.com:"]
insteadOf = https://gitlab.com/
[url "https://"]
insteadOf = git://
[url "http://"]
insteadOf = git://
or execute:
git config --global url."https://".insteadOf git://
git config --global url."http://".insteadOf git://
git config --global url."git@gitlab.com:".insteadOf https://gitlab.com/
git config --global url."git@github.com:".insteadOf https://github.com/
Set proxies executing the following lines in the bash command-line environment:
export ftp_proxy=http://proxyout.<your_site>:8080
export rsync_proxy=http://proxyout.<your_site>:8080
export http_proxy=http://proxyout.<your_site>:8080
export https_proxy=http://proxyout.<your_site>:8080
export no_proxy=.<your_site>
For example, at LANL, you will need to execute the following lines in the bash command-line environment:
export ftp_proxy=http://proxyout.lanl.gov:8080
export rsync_proxy=http://proxyout.lanl.gov:8080
export http_proxy=http://proxyout.lanl.gov:8080
export https_proxy=http://proxyout.lanl.gov:8080
export no_proxy=.lanl.gov
Proxies can be also set up directly in the Julia REPL as well:
ENV["ftp_proxy"] = "http://proxyout.lanl.gov:8080"
ENV["rsync_proxy"] = "http://proxyout.lanl.gov:8080"
ENV["http_proxy"] = "http://proxyout.lanl.gov:8080"
ENV["https_proxy"] = "http://proxyout.lanl.gov:8080"
ENV["no_proxy"] = ".lanl.gov"
Related Julia Packages
- TensorDecompositions: Unsupervised Machine Learning based on Matrix/Tensor Factorization
- RegAE: Regularization with a variational autoencoder for inverse analysis
- Geostatistical Inversion with randomized + sketching optimization