For the last six decades, engineering design has been advanced through the synergistic and principled use of theory, experiments, and physics-based simulations. Our increased ability to sense, acquire and analyze data is clearly a game-changer -- from data analytics and machine learning to digital twins, engineering design is becoming increasingly data-centric. Yet in our excitement to define a new generation of data-centric engineering approaches, we must be careful not to chart our course based entirely on the successes of data science and machine learning in the vastly different domains of computer science where data are plentiful and physics-based models do not exist. This talk will draw examples from digital twins and reduced-order modeling to illustrate that data-centric engineering must bring together the perspectives of data-driven learning and physics-based modeling.
Karen Willcox is Director of the Oden Institute for Computational Engineering and Sciences, Associate Vice President for Research, and Professor of Aerospace Engineering and Engineering Mechanics at the University of Texas at Austin. She is at the forefront of new physics-based and data-driven computational methods to advance engineering design and uncertainty quantification.
|Date||28th October 2021|
|Organisation||University of Texas at Austin|