Scientific Machine Learning for Predictive Digital Twins of Complex Systems | Peng Chen | BBISS

Predictive digital twins virtually represent complex physical systems by learning predictive models of the system from sensor data and enable decision-making to optimize future behavior under uncertainty. Peng Chen will present the key technology of scientific machine learning to enable predictive digital twins with applications to geoscience, materials science, natural hazards.
Peng Chen is an assistant professor at the School of Computational Science and Engineering. His research is driven by challenging problems that involve data-driven modeling, learning, and optimization of complex systems under uncertainty, and focuses on scientific machine learning, uncertainty quantification, Bayesian inference, experimental design, and stochastic optimization.
Connect with Peng Chen:
Profile: www.cc.gatech.edu/people/peng...
SciML & UQ Group: faculty.cc.gatech.edu/~pchen402/
Connect with BBISS:
Website: sustainable.gatech.edu
LinkedIn: / georgia-tech-bbiss
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