Machine Learning Data-Driven Discretization Theories, Modeling and Applications: SEMM Seminar
Seminar | February 4 | 12-1 p.m. | 502 Davis Hall
Wing Kam Liu, PhD, PE, Northwestern University/Global Center on Advanced Material Systems and Simulation
An open problem in data-driven methods for mechanical science is the efficient and accurate description of heterogeneous material behavior that strongly depends on complex microstructure. To explore the future development and the adaptation of data-driven methods, new mathematical and computational paradigms and broad flexible frameworks are needed, which can lead to probabilistic predictions using the minimum amount of information that can be processed expeditiously and be sufficiently accurate for decision making under uncertainty. Integrating multi-fidelity data into large-scale simulations is necessary to speed up the computation but also to deal with the hidden physics not captured by the lack of resolution or the lack of proper constitutive laws or boundary conditions. A number of applications will be presented.