MADS provides everything you need to better understand and utilize your models and the associated data.


MADS is an open-source software toolbox providing methods for model analyses and diagnostics.

MADS can be coupled with any model. The analyzed models can represent any physics, including hydrological, environmental, and climate models. They can be numerical, analytical, or ML-developed models. Efficient parallel computing is supported for analyses of large-scale models.

MADS can be applied to process any type of data, including time series, spatial data, and images.

MADS includes a wide range of visualization and mapping tools.

MADS can be applied for model evaluation, calibration, sensitivity analysis, uncertainty quantification, and decision support.

MADS uses cutting-edge methods to perform these analyses, including Affine Invariant MCMC and Bayesian-Information-Gap Decision Theory.

MADS includes advanced visualization and mapping tools for processing inputs and outputs.

MADS has been extensively tested and verified. MADS has been applied to a wide range of models and data.

MADS is developed in Julia and is available on GitHub. Various tutorials and examples are available on the MADS website and on the GitHub pages. They will allow users to get started with MADS quickly. Do not hesitate to contact us if you have any questions, concerns, or suggestions.