Join us for the Harrington Fellow Symposium:
Scientific Machine Learning for Computational Mechanics
Over the past two decades, improvements in computational power, software, and data availability have significantly expanded the role of Artificial Intelligence (AI) in engineering applications. Initially prominent in image processing and informatics, AI methods are now increasingly applied to directly solve ordinary and partial differential equations (ODEs, PDEs) and constitutive equations in computational mechanics. For example, neural networks have been trained to emulate or replace traditional physics solvers and closed-form energy potentials. Other machine learning (ML) techniques, such as Gaussian processes, reduced order models, and automated model discovery, have also been adopted and further developed to work specifically in the context of computational mechanics.
The symposium objective is to bring together the leading experts in scientific machine learning for computational mechanics and synthesize directions for research and education in the United States in this field of science over the next decade and beyond.
Thursday, May 15 through Friday, May 16
8:00 am - 5:00 PM
Avaya Auditorium, Peter O'Donnell Jr. Building (POB 2.302)
201 E. 24th Street
Austin, TX 78712
(insert link or full address)
KEYNOTE SPEAKERS
Karen E. Willcox
Professor, Department of Aerospace Engineering and Engineering Mechanics
Director, Oden Institute for Computational Engineering and Sciences
George Karniadakis
Professor of Applied Mathematics and Engineering, School of Engineering, Brown University
SYMPOSIUM ORGANIZERS
Adrian Buganza Tepole
Associate Professor of Mechanical Engineering, Purdue University
Harrington Faculty Fellow, The University of Texas at Austin
Adrian Buganza Tepole is an associate professor at Purdue University in the School of Mechanical Engineering and the Weldon School of Biomedical Engineering. His group studies the interplay between mechanics and mechanobiology of soft tissue, with skin as a model system. Using computational simulation, machine learning, and experimentation, his group seeks to characterize the multi-scale mechanics of tissues to understand the fundamental processes of mechano-adaptation in order to improve clinical diagnostics and interventional tools. His current work focuses on data-driven methods for modeling tissues that satisfy physics by default through the design of novel machine-learning architectures. A central application of these tools is the creation of predictive models of breast reconstruction after mastectomy and lumpectomy treatments for breast cancer, the most common cancer in women.
Manuel K. Rausch
Associate Professor, Department of Aerospace Engineering and Engineering Mechanics, The University of Texas at Austin
Jan Fuhg
Assistant Professor, Department of Aerospace Engineering and Engineering Mechanics, The University of Texas at Austin
MAPS & HOTELS
Maps
The University of Texas at Austin.
Hotels in the Area
AT&T Hotel and Conference Center
Moxy Austin - University
AC Hotel Austin - University
Hampton Inn & Suites - University/Capitol
Learn more about this year's Harrington Faculty Fellows.
Contact Adrian Buganza Tepole for more information.
