What is Predictive Data Science?

Predictive data science is a convergence of the fields of Data Science and Computational Science & Engineering.

Predictive data science is needed for high-consequence applications across science, engineering and medicine, where machine learning approaches based on data alone are insufficient.

predictive data science: connvergence of Data Science and Computational Science & Engineering.

Resources on Predictive Data Science

Download slides [PDF] from Prof. Willcox's invited talk at the 2019 International Congress on Industrial and Applied Mathematics (ICIAM)

Watch [short video] Prof. Willcox explain the importance of Predictive Data Science

Watch the full invited ICIAM talk on Predictive Data Science

Learn more about Predictive Data Science at the Oden Institute

Papers by the Willcox Research Group in Predictive Data Science:

McQuarrie, S., Huang, C. and Willcox, K., Data-driven reduced-order models via regularized operator inference for a single-injector combustion process. arXiv preprint arXiv:2008.02862, 2020.
Kapteyn, M., Knezevic, D., Huynh, D.B.P., Tran, Minh and Willcox, K., Data-driven physics-based digital twins via a library of component-based reduced-order models. To appear, International Journal for Numerical Methods in Engineering, 2020.
Qian, E., Kramer, B., Peherstorfer, B., and Willcox, K., Lift & Learn: Physics-informed machine learning for large-scale nonlinear dynamical systems. Physica D: Nonlinear Phenomena, Volume 406, May 2020, 132401.
Swischuk, R., Kramer, B., Huang, C., and Willcox, K., Learning physics-based reduced-order models for a single-injector combustion process. AIAA Journal, Vol. 58, No. 6, June 2020, pp. 2658-2672.
Kramer, B. and Willcox, K., Nonlinear model order reduction via lifting transformations and proper orthogonal decomposition. AIAA Journal, Vol. 57 No. 6, pp. 2297-2307, 2019.
Qian, E., Kramer, B., Marques, A. and Willcox, K., Transform & Learn: A data-driven approach to nonlinear model reduction. In Proceedings of AIAA Aviation Forum & Exhibition, Dallas, TX, June 2019.
Peherstorfer, B. and Willcox, K., Data-driven operator inference for nonintrusive projection-based model reduction, Computer Methods in Applied Mechanics and Engineering, Vol. 306, pp. 196-215, 2016.