Department of Mechanics: Seminar: Abstract Liu 2019

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Mechanistic Machine Learning Methods for Mechanical Science and Design/Optimization of Lightweight Material Systems

Wing Kam Liu, Northwestern University, Evanston, Illinois, USA

Room B-366, Faculty of Civil Engineering, CTU in Prague

Friday, 6 September 2019, 11am

As in all everyday applications, in engineering problems, the volume of data has increased substantially compared to even a decade ago but analyzing big data is expensive and time-consuming. Data-driven methods, which have been enabled in the past decade by the availability of sensors, data storage, and computational resources, are taking center stage across many disciplines (physical and information) of science. We now have highly scalable solutions for problems in object detection and recognition, machine translation, text-to-speech conversion, recommender systems, and information retrieval. All of these solutions attain state-of-the-art performance when trained with large amounts of data. However, purely data-driven approaches for machine learning present difficulties when the data is scarce and of variable fidelity relative to the complexity of the system.

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 material systems applications will be presented.

Professor Wing Kam Liu is the Walter P. Murphy Professor of Northwestern University, Director of Global Center on Advanced Material Systems and Simulation, President and Past President of the International Association for Computational Mechanics (IACM) (President (2014-2018) Past President (2018-2024)), Past Chair (2017-2018) (Chair 2015-2016) of the US National Committee on TAM and Member of Board of International Scientific Organizations, both within the US National Academies. Selected synergistic activities includes the development of ICME multiscale theories, methods, and software with experimental validations for the analysis, design and manufacture of engineering material systems, materials design, advanced and additive manufacturing; and technology transfer. He has over 38 years of engineering and manufacturing consulting, including a broad array of companies and industries, small businesses, and international corporations. Liu’s selected honors include Japan Society of Computational Engineering Sciences Grand Prize; Computational Mechanics Award from Japanese Society of Mechanical Engineers; Honorary Professorship from Dalian University of Technology, IACM Gauss-Newton Medal (highest honor) and Computational Mechanics Award; ASME Dedicated Service Award, ASME Robert Henry Thurston Lecture Award, ASME Gustus L. Larson Memorial Award, ASME Pi Tau Sigma Gold Medal and ASME Melville Medal; John von Neumann Medal (highest honor) and Computational Structural Mechanics Award from the US Association of Computational Mechanics (USACM). He was the founding Director of the NSF Summer Institute on Nano Mechanics and Materials and Founding Chair of the ASME NanoEngineering Council. He is the editor of two International Journals and honorary editor of two journals and has been a consultant for more than 20 organizations. Liu has written four books; and he is a Fellow of ASME, ASCE, USACM, AAM, and IACM.