Data-Driven Computational Science
PresentationIn many fields of science and engineering, decisions are based on the outcomes of models that estimate/predict the state of a physical system or some of its relevant properties. One can distinguish two main families of such predictive models:
- Data-Driven models that are learnt from possibly noisy observation measurements,
- Physics-Based models which are usually expressed in the form of a Partial Differential Equations. The PDE formulations rely on first physical principles, and are usually solved by Computational Science methods.
We are a research group of applied mathematicians striving to develop a coherent mathematical and algorithmic framework that optimally combines the strengths of complex physics-based models with the (often vast) data sets which are now routinely available in many fields of engineering, science and technology. The main challenges that we face are:
- the high dimensionality of the involved mathematical objects,
- the heterogenous nature and the noise of the available data,
- the underlying optimization problems is often neither convex nor smooth.
Where to find us
- We are based at TU Eindhoven
- We belong to its Mathematics & Computer Science Department
- More specifically, we are embedded in CASA, the Center for Analysis, Scientific Computing and Applications