Brandi Klingerman | March 10, 2018
Dramatic advances in data sciences, machine learning and scientific computing, as well as the growing ability to collect scientific data, has led to a need for improved predictive modeling and design of complex systems. In order to better characterize the predictability of computational models and product performance, a new research center at the University of Notre Dame, the Center for Informatics and Computational Science (CICS), will develop mathematical, statistical and scientific computing techniques to address the challenges associated with uncertainty quantification.
In explaining the new center, Nicholas Zabaras, Viola D. Hank Professor of Aerospace and Mechanical Engineering and founding director of the CICS, said, “At the CICS, we want to be able to deliver computational models that quantify uncertainties across multiscale and multiphysics models, but also identify the most informative computational or experimental data needed to minimize uncertainties in predictions or design performance. From aerospace to pharmaceuticals, our work will be applicable to many industries for improving product reliability as well as for accelerating product development by minimizing unnecessary experimental testing or maintenance.”
Data-driven computational modeling is an imperative component of modern simulations that can significantly increase their predictive accuracy, and such modeling is essential when designing products that need to perform with relative consistency in often uncertain environments. The center will emphasize unifying themes in the mathematical and statistical sciences, as well as scientific computing, which are necessary for the predictive modeling and design of complex systems within disciplines such as physics, chemistry, biology and engineering.