## Data-Driven Computational Science

#### Presentation

In 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. Our research activities include:

**Approximation and Learning:**model reduction, neural networks, tensor methods**Inverse Problems and Data Assimilation:**optimal reconstruction schemes, sensor placement**Numerical Optimal Transport****Numerical Analysis of PDEs:**numerical solution of kinetic models, a posteriori error estimation, domain decomposition**Applications:**haemodynamics, pollution, epidemiology, nuclear engineering

#### Team Members

#### 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