Professor Dr Gert Aarts is known for his work on strongly interacting matter and for linking theoretical physics with machine learning. Wübben Stiftung Wissenschaft is funding Professor Gert Aarts’ appointment as Professor of Theoretical Physics at Bielefeld University with 1.58 million Euros over four years. Aarts assumes the position in August.

Discovering the materials needed for cleaner energy technologies has long been a slow process - but AI could change that. Researchers at Tohoku University have developed a closed-loop AI framework that continuously learns from experiments to identify promising energy materials faster and at lower cost. The approach could accelerate the development of next-generation batteries, hydrogen storage and other low-carbon technologies. 

Relevant technological advancements in recent years have established solar sailing as an attractive propulsion system for terrestrial and interplanetary space missions, particularly missions that require low-thrust propulsion for long periods of time. The initial stages in designing solar sail missions involve a thorough analysis of possible transfer trajectories. Traditional approaches primarily focus on deterministic scenarios where the spacecraft dynamics is precisely defined and fully characterized. Whereas, space trajectories are inevitably subject to various sources of uncertainty, including modeling errors in the dynamics, inaccurate measurements, and unpredictable environmental effects, such as fluctuations in solar irradiance. These uncertainties necessitate the development of robust control strategies that can effectively handle stochastic effects. In recent years, reinforcement learning (RL) offers a promising framework for addressing optimization problems characterized by significant uncertainty, which can be challenging to handle using traditional methods. Meanwhile, deep reinforcement learning (DRL) utilizes neural networks (NNs) to approximate complex policies, i.e. control laws. 

Sudden surface anomalies (SSAs)—ranging from earthquakes and floods to wildfires and industrial accidents—pose escalating threats to ecosystems and societies worldwide. With its advantages of wide coverage, high revisit frequency, and rich information content, remote sensing is regarded as an effective tool for rapid detection and early warning of such anomalies. However, significant technical bottlenecks remain in using remote sensing to monitor SSAs, such as delays due to revisit intervals and complex data processing chains and challenges for the integration and joint analysis of multi-source information, resulting in frequent occurrences of missed detections and false alarms. Real-time remote sensing has emerged as a transformative approach for monitoring and early warning of such abrupt events, driven by recent advances in satellite constellations, on-orbit artificial intelligence, and multi-source data fusion.

The battle against environmental degradation is complex. Green technologies can be both part of the solution and part of the problem, a recent study has revealed. Researchers from Tohoku University and the National Institute for Environmental Studies have utilized remote sensing and machine learning to reveal the impacts mining for minerals in green technologies has on biodiversity.