Mini-symposium on “Data-Enabled Predictive Modeling, Machine Learning, and Uncertainty Quantification in Computational Mechanics” at IMECE 2020 conference

Dear Colleagues,

As part of the IMECE 2020 (November 13-19, 2020, Portland, Oregon), we are organizing a topic on “Data-Enabled Predictive Modeling, Machine Learning, and Uncertainty Quantification in Computational Mechanics.” It is listed in Track 12: Mechanics of Solids, Structures, and Fluids: https://event.asme.org/IMECE/Program/Tracks-Topics.

You are cordially invited to submit an abstract to this topic. This mini-symposium focuses on the recent developments of novel machine learning methods and predictive physics-based computational models. The topics of interest include but not limited to data science, model validation, uncertainty quantification, real-time assimilation of data, reduced-order modeling, optimal design of experiments, Bayesian inference, as well as design, control, and decision making under uncertainty.

The presentation only abstract submission deadline is July 13, 2020. Abstracts must be submitted online at https://event.asme.org/IMECE.

We hope that you’ll be able to attend and present your work at the conference.

Best regards

Danial Faghihi, University at Buffalo

Alireza Tabarraei, University of North Carolina at Charlotte 

Kathryn Maupin, Sandia National Laboratories