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

The Deep Infinite Mixture of Gaussian Processes (DIM-GP) algorithm addresses this challenge by enabling the domain experts to efficiently address a variety of advanced technological problems. In contrast to other algorithms, DIM-GP had been 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 our STOCHOS© software package. This integration ensures a cohesive collaboration of algorithms, collectively pushing the boundaries of what is achievable in the specific field.