The availability of very large data sets offers new possibilities and challenges for research within SACS.
Data-driven science allows the discovery of hidden parameters or functions in dynamical systems from observed data by solving (ill-posed) inverse problems. Also, topics such as uncertainty quantification and model order reduction are rapidly gaining importance. We aim therefore to combine data-driven science with our strong research in dynamical systems, scientific computing, and mathematical systems theory. This will offer new opportunities to obtain more accurate data-driven models in for instance neuroscience, seismic, and turbulence, but also present important mathematical challenges, such as understanding the mathematical properties of these new models and techniques and deriving and analyzing accurate and efficient numerical discretizations for these novel approaches.
The SACS research team focuses on the following topics in the area of dynamical systems, numerical analysis and scientific computing, and systems and control, and in particular, their relation with data science.
- Structure preserving numerical discretizations.
- Inverse problems, data assimilation, and optimal transport on graphs.
- Discovery of equations, model sparsity, and uncertainty, deep machine learning for PDEs.
- Control of partial differential equations
- Dynamical Systems and Clinical Neuroscience
- Scientific Machine Learning (SciML)
- Multi Scale Modeling
SACS is contributing to NDNS+, one of the four mathematics clusters in the Netherlands. Our work is part of the JM Burgers Center for Fluid Mechanics and the DISC research school on Systems and Control. We are active partners of 4TU.AMI, the Applied Mathematics Institute of the three Universities of Technology and the University of Wageningen, and of PWN, the Netherlands Mathematics Platform.
Our teaching program prepares students for working in academia and in industry, strengthened by our unique emphasis on close multidisciplinary collaboration.