Predictive Data Science for Physical Systems: From Model Reduction to Scientific Machine Learning
TimeThursday, 21 November 20199:15am - 10am
DescriptionAchieving predictive data science for physical systems requires a synergistic combination of data and physics-based models, as well as a critical need to quantify uncertainties. For many frontier science and engineering challenge problems, a purely data-focused perspective will fall short---these problems are characterized by complex multi-scale multi-physics dynamics, high-dimensional uncertain parameters that cannot be observed directly, and a need to issue predictions that go beyond the specific conditions where data may be available. Learning from data through the lens of models is a way to bring structure to an otherwise intractable problem: it is a way to respect physical constraints, to embed domain knowledge, to bring interpretability to results, and to endow the resulting predictions with quantified uncertainties. This talk highlights how physics-based models and data together unlock predictive modeling approaches through two examples: first, building a Digital Twin for structural health monitoring of an unmanned aerial vehicle, and second, learning low-dimensional models to speed up computational simulations for design of next-generation rocket engines.