# Data, Models, and Mathematics

Technological progress has expanded the data-collection capabilities enormously and continues to do so. Clearly, it is not the data per se that create value. What really matters is the ability to derive from them new insights, to recognize relationships, and to make increasingly accurate predictions.

To this end, mathematical methods have been and are being developed making them an integral and essential component of basic and applied research in science, technology, industry, and economy. This involves the integration of mathematics, statistics, and computation in the broadest sense, and the interplay of these areas with applications.

Computing is often the means by which the mathematical sciences are applied to other fields. For example, mathematicians collaborate with scientists working in fluid dynamics, material science, molecular biology, quantum physics, or semiconductor physics to develop new models and simulation software to understand complex phenomena.

The common goal is to capture pertinent features of a problem by abstract structures through the process of modeling, performing formal reasoning on these abstract structures or using them as a framework for computation, and then reconnecting back to make predictions. Clearly, this is an inherently iterative process.

Therefore, a new algorithm can be as powerful an enhancement to resolution as a new instrument. Additionally, the data that can be measured are not always the data that one ultimately wants. This results in what is known as an inverse problem: the process of collecting data imposes a very complicated transformation on the data one wants, and a computational algorithm is needed to invert the process.

Not all data are numerical – they may be, e.g., categorical, qualitative or visual describing shape. Hence perspectives and techniques for dealing with such data and with their uncertainties are also of interest.

This part of the Vienna young Scientists Symposium will give an overview of the manifold activities at TU Wien related to the interplay of data, models, and mathematics. We interpret the topic in the wide sense sketched above including experiments, representation and analysis of various types of data, modeling, mathematical analysis, and computation. In addition the minisymposium aims at stimulating the exchange of ideas between different research fields. Hence contributions from research areas where the links from data via models to mathematics are not yet well explored or are just emerging are particularly welcome.

**Chair und Reviewer**: Associate Prof. Dipl.-Ing. Dr.techn. Christoph Lemell and Univ.Prof. Dipl.-Ing. Dr.techn. Peter Szmolyan