Menu Close

NCSA Leverages DeepONet, Transforming ML Models in Structural Mechanics

New challenges to make AI and machine learning (ML) live up to their promise

Seid Koric, Technical Assistant Director, National Center for Supercomputing Applications

Seid Koric, the technical associate director of NCSA’s Research Consulting directorate and research associate professor in the Department of Mechanical Science and Engineering (MechSE) at the University of Illinois, is leading a team of researchers at NCSA and The Grainger College of Engineering that stands at the forefront of AI and ML advances. With collaborators from Khalifa University of Science and Technology in Abu Dhabi, UAE, they’ve used high-performance computing to generate data and train novel AI models. The high-throughput and parallel capabilities of HPC decrease the time and expense needed for data generation and ML training by orders of magnitude. Their novel research, which they explain in a paper published in May in the prestigious journal Engineering with Computers, applies a recently introduced deep learning operator network, or DeepONet, in nonlinear and irreversible plastic behavior exhibited by many engineering materials under large deformation. Due to the outstanding scientific value of the article, the publisher (Springer Nature) has given free open access to the article for two months.

To read more go here.