Gaussian process regressions and simulations. Kriging is a module of MADS.
- Sensitivity Analysis
- Parameter Estimation
- Model Inversion and Calibration
- Uncertainty Quantification
- Model Selection and Model Averaging
- Model Reduction and Surrogate Modeling
- Machine Learning and Blind Source Separation
- Decision Analysis and Support
MADS has been tested to perform HPC simulations on a wide-range multi-processor clusters and parallel environments (Moab, Slurm, etc.). MADS utilizes adaptive rules and techniques which allows the analyses to be performed with a minimum user input. The code provides a series of alternative algorithms to execute each type of data- and model-based analyses.
All the available MADS modules and functions are described at madsjulia.github.io
Installation behind a firewall
Julia uses git for the package management.
To install Julia packages behind a firewall, add the following lines in the
.gitconfig file in your home directory:
[url "https://"] insteadOf = git://
git config --global url."https://".insteadOf git://
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, if you are doing this at LANL, you will need to execute the following lines in your 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
In Julia REPL, do the following commands:
To explore getting-started instructions, execute:
There are various examples located in the
examples directory of the
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.