Navigating machine learning challenges requires selecting the right algorithm and understanding diverse data, from numerical values like chemical formulations to complex entities like FEM simulations or 3D material properties.

The DIM-GP (Deep Infinite Mixture of Gaussian Processes) algorithm addresses this challenge by enabling users, even those with limited expertise, to efficiently address a variety of advanced technological problems. In contrast to other algorithms, DIM-GP is designed to handle vast datasets with minimal hardware requirements. Its notable capability, however, lies in constructing highly accurate models from limited data points—an essential feature given the high costs and scarcity of real experiments and simulations.

To complement DIM-GP, a suite of algorithms for optimized experimentation, efficient optimization, and sensitivity analysis has been seamlessly integrated into the Stochos software package. This integration ensures a cohesive collaboration of algorithms, collectively pushing the boundaries of what is achievable in the field.