The Laboratory for Composite AI specializes in automated AI methods, namely, automated machine learning, automated design of physical objects, mathematical physics equations based on data, etc. Composite AI is a breakthrough approach combining a range of computational models, including AI and more traditional models.

Accomplishments

• FEDOT – an automated machine learning framework;
• EPDE – a partial differential equations discovery framework;
• BAMT – a framework for generating Bayesian networks;
• GEFEST – a toolbox for the generative design of physical objects.

Partners

• Gazprom Neft;
• Gazprom Neft’s Science and Technology Center;
• Rosneft.

Publications

• Maslyaev M., Hvatov A., Kalyuzhnaya A. V. Partial differential equations discovery with EPDE framework: Application for real and synthetic data // Journal of Computational Science, 2021, vol. 53., pp. 101345;
• Nikitin N. O. et al. Automated evolutionary approach for the design of composite machine learning pipelines //Future Generation Computer Systems, 2022, vol. 127, pp. 109-125;
• Deeva I., Bubnova A., Kalyuzhnaya A. V. Advanced Approach for Distributions Parameters Learning in Bayesian Networks with Gaussian Mixture Models and Discriminative Models // Mathematics, 2023, vol. 11, No. 2, pp. 343;
• Starodubcev N. O. et al. Generative design of physical objects using modular framework // Engineering Applications of Artificial Intelligence, 2023, vol. 119, pp. 105715.