Sich in der Vielzahl aktuell angebotener Algorithmen für maschinelles Lernen zurechtzufinden ist eine anspruchsvolle Aufgabe.Die Herausforderung besteht nicht nur darin, den passenden Algorithmus auszuwählen, sondern auch die Mannigfaltigkeit zugrunde liegender Daten zu überschauen und zu einzuordnen – von numerischen Werten und akustischen Signalen bis hin zu komplexeren Formen wie transienten FEM-Simulationen oder 3D-Materialeigenschaften.

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