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New Class on Physics-based and Data-Driven Reduced-Order Modeling for Engineering Systems

This course covers the introduction to surrogate and reduced order modeling to speed up engineering workflows, including, but not limited to, multiphysics applications in engineering where reduced-order modeling can mitigate many expensive computations. Such examples include: reservoir simulation, production optimization, complex multiphase flow simulation, neutron diffusion, coupled neutronics/thermal-hydraulics, nuclear heat transfer, and other pertinent areas. It is intended primarily for graduate students interested in any computational science/ engineering application. The course material is complemented by a balanced set of theoretical, algorithmic, and MATLAB/Python computer programming homework’s and assignments. Invited lectures from researchers and professionals in model reduction will be given as time permits.

Please contact Dr. Gildin ( or Dr. Ragusa ( for more information on the class.