MADS (Model Analysis & Decision Support)
MADS is an integrated open-source high-performance computational (HPC) framework in Julia for data analytics and model diagnostics.
MADS can execute a wide range of data- and model-based analyses:
- 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 tested to perform HPC simulations on multi-processor clusters, parallel and cloud computing environments (including Moab, Slurm, etc.).
MADS utilizes adaptive rules and techniques that allow the analyses to be performed with minimum user input.
MADS provides a series of alternative algorithms to execute various types of data- and model-based analyses implemented in the code.
Publications, Presentations, Projects
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
git clone email@example.com:madsjulia/Mads.jl(recommended)
git clone firstname.lastname@example.org:mads/Mads.jl(might not be up-to-date)
docker run --interactive --tty montyvesselinov/madsjulia
- email: email@example.com