Advances in networking, sensor technologies, and computer science have enabled the collection of gigantic amounts of data at ever accelerating rates. At the same time, developments in machine learning and computational statistics have also led to powerful tools for extracting patterns and trends from the data. Most of these outputs are mapped to predictive models of increasing complexity and involving huge numbers of parameters.
In parallel, computational scientists in physics, chemistry, biology, and engineering have been experiencing their own Big Data revolution. Due to the sophistication of the mathematical models and the availability of high-performance computing platforms, researchers at Notre Dame can now simulate physical processes at unparalleled levels of spatio-temporal resolution. With every simulation, scholars see exuberant growth of output data. Yet, despite their sophistication, such models are not always predictive. The lack of predictability can be attributed to various parametric and model uncertainties, as well as to vast differences between the scales and availability of accurate simulation tools.
Data-driven computational modeling has been recognized as an indispensable component of modern simulations that can significantly increase their predictive accuracy by leveraging model calibration and model validation methodologies as well as by efficiently propagating input uncertainty to the outputs.
The primary goals of research as the CICS is the synthesis of ideas, models, and algorithmic frameworks in order to advance the automated extraction of useful information from huge amounts of experimental or simulation data, the utilization of data in order to make predictions at scales that are currently inaccessible, and the development of scalable and accurate methodologies for high-dimensional uncertainty propagation through physics-based models.
The CICS supports fundamental research in mathematics, statistics, and machine learning to advance the state-of-the-art in data-driven, predictive, computational modeling of complex systems in science and engineering.
Research at the CICS can be categorized into the following two areas: