Data Driven Computational Science

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.

Developing a coherent mathematical and algorithmic framework optimally combining the strengths of complex physics-based models with (often vast) data sets.

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:

Meet some of our Researchers

Associate Professor

Olga Mula Hern谩ndez

Assistant Professor

Rafael Bailo

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Recent Publications

Our most recent peer reviewed publications