Research Pillars
Green AI
The Green AI pillar focuses on developing energy-efficient AI methods and metrics to reduce the carbon footprint of AI systems, from low-resource embedded devices to large cloud platforms. It aims to enable sustainable AI solutions that maintain performance while minimizing environmental impact, supporting climate action and green digital transformation goals.
Project: Developing a framework for green AI practices
Third parties involved: Maria Ulan – RISE Research Institutes of Sweden
The project aims to define and standardize green AI metrics across the entire AI lifecycle. It will review existing metrics in sustainable software engineering, propose standardized calculations, and introduce new metrics where current ones are inadequate. These metrics will be evaluated and validated. The project will also outline the data requirements and functionalities for a benchmarking tool to minimize AI’s environmental impact. Expected outcomes include a robust open-source green AI metrics framework and its application to assess AI architectures for energy efficiency and performance. This will provide a foundation for European policymakers to develop regulations and eco-labels that promote environmentally responsible AI development and deployment.
Project: Information-theoretic analysis of generalization in deep learning
Third parties involved: Linara Adilova – Ruhr University Bochum
The project aims to apply information-theoretic analysis—specifically, information planes—to deep neural networks to predict their generalization and potentially optimize their performance and size. It will first select a suitable mutual information estimation approach for deep neural networks, addressing challenges of high dimensionality and computational complexity. Using this approach, the project will then assess the capability of information-plane analysis to explain a network’s generalization through a large, carefully curated set of experiments. Finally, it will propose and evaluate a method for optimizing network architectures for given tasks.
Project: Advancing World model Learning with Neural Cloned-Structured Causal Graphs
Third parties involved: Tristan Manfred Stöber – Ruhr University Bochum
The project pioneers a neural implementation of Cloned-Structured Causal Graphs (CSCGs), an algorithm for learning world models from sequential observations. By reformulating CSCG as a deep learning problem, it demonstrates seamless integration of a neural CSCG module with a vector-quantised variational autoencoder (vqVAE) and its ability to produce rich latent representations from visual input. The flexibility of this neural approach enables joint training of the vqVAE and neural CSCG to test whether explicit world models can improve prediction of action consequences. It aims to deliver significant advances in autonomous, reliable, and data-efficient artificial intelligence.
Project: Optimal hybrid AI strategies for battery management and state estimation
Third parties involved: Lluís Trilla, Andrés Bernabeu Santisteban – IREC (Catalonia Energy Research Institute)
This project analyzes hybrid AI strategies for battery management and state estimation by combining reduced-order physics-based models with machine learning. A key objective is to balance model granularity with computational efficiency for optimal estimation of SoC and SoH. Numerical studies will be complemented by experimental validation at IREC and KNOW Center. Expected outcomes include low-cost algorithms, efficient state estimation, and modular hybrid models that advance sustainable energy solutions for electric mobility and energy storage.
Project: Hybrid AI Model for Mixed-Gas Sorption Upper Bounds
Third parties involved: Tristan Manfred Stöber – Ruhr University Bochum
The project aims to build a hybrid modelling framework for mixed-gas sorption in polymer membranes for environmental applications, including carbon capture and hydrogen separation. It integrates physics-based and data-driven approaches. The project also evaluates multiple representation methods—such as molecular graphs and chemical language models—to develop machine-learning models that predict the parameters of a physics-based SAFT equation of state. The resulting integrated ML/SAFT framework will be released as open-source software and used to generate new mixed-gas performance plots. These plots will guide membrane scientists in materials selection, advancing membrane technology in Europe and beyond.