Navigating the landscape of machine learning algorithms is a challenging task. The difficulty arises not only in selecting the appropriate algorithm but also in understanding the varied nature of the underlying data—ranging from numerical values and acoustic signals to more complex forms such as transient FEM simulations or 3D material properties.

As the number of available algorithms continues to expand, so does the need for specialized expertise. DIM-GP addresses this challenge by providing a tool that allows users, even those with limited experience, to effectively tackle a broad range of problems in advanced technology
fields.
DIM-GP is designed to handle large datasets efficiently on modest hardware, while its key strength lies in building accurate models from relatively small datasets. This is particularly valuable given the high cost and limited availability of real-world experiments and simulations.

To enhance DIM-GP’s capabilities, a set of algorithms for optimized experimentation, efficient optimization, and sensitivity analysis has been integrated into the Stochos software package. This integration facilitates the smooth interaction of various tools, broadening the scope of possible applications