Area of Interest
Hovedinnhold
We are interested in (but not limited to) contributing on the following areas to address challenges related to sustainable energy production, distribution and saving:
Machine Learning
In machine learning, we have two main areas of interest. The first one is the use of geometric and topological methods to improve the effectiveness of training machine learning models. Currently many models are have to learn known symmetries in data. Geometric machine learning is concerned with learning under symmetry constraints and geometric methods can achieve better performance with less data and shorter training time.
A second focus area is decision-making under uncertainty. We study uncertainty propagation in machine learning and simulation methods. A particular interest are multi-modal distributions and how to summarize them in meaningful ways. These arises for example in the context of weather prediction, with applications to wind and solar energy.
For more information, please contact Nello Blaser.
Optimization
In optimization, we are interested in designing efficient and robust solutions for large-scale problems that exist in energy section.
For more information, please contact Ahmad Hemmati.
Green and High Performance Computing
For more information, please contact Magne Haveraaen.
Master's students
Do you want to write a master’s thesis within the field of Energy informatics?
If you are an energy student who is interested in informatics or an informatics student who is interested in energy, you should consider doing your project in energy informatics.
You should contact us early in your master’s programme so we can make a plan to create a great project.
