Digital Twin Lab


Fall 2023

CSCE 645 / VIZA 675 – Geometric Modeling

Course Title: Geometric Modeling
Time: 5:30 pm – 6:45 pm on Tuesday and Thursday
Location: HRBB 126
Credit Hours: 3 hours

Instructors: Dr. Wenping Wang

This course provides a comprehensive foundation in geometric computing and shape modeling for graduate students who study or conduct research in visual computing, including computer graphics, computer vision, computer animation, VR/ CAD/CAM, scientific data visualization, and medical imaging.

The course will introduce the basic concepts of Euclidean geometry, affine geometry, projective geometry, and differential geometry, from a computational point of view. It covers geometric representations commonly used in visual computing, including Bezier curves and surfaces, B-spline curves and surfaces, subdivision surfaces, mesh surfaces, point cloud surfaces, and implicit surfaces, as well as the emerging neural implicit surface representation.

PETE 689 / NUEN 689 – Physics-based and Data-Driven Reduced-Order Modeling for Engineering Systems


CRN: 55531 (PETE)
Time: 02:20 PM-05:10 PM, Thu
Location: 313 RICH
Credit Hours: 3

Instructors: Drs. Eduardo Gildin and Jean Ragusa

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. Model reduction, proxy modeling and surrogate modeling and their variants are becoming an indispensable tool for computational-based design and optimization, statistical analysis, embedded computing, and real-time optimal control. This course will present a survey on physics-based model reduction for large scale dynamical systems and on data-driven modeling based on surrogate and machine learning techniques. The course material described below 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.

Spring 2023

VIZA 689 – Introduction to Digital Twin Tech


CRN: 47532
Time: MW 1:50pm – 3.30pm
Location: LAAH 124
Credit Hours: 3

Instructor: Dr. Jian Tao

This course will give a comprehensive introduction to digital twins and the technologies to make them possible. Specifically, the course will briefly introduce the tools and techniques involved in creating representation models and user interfaces for digital twins. It will also cover the basics of the internet of things, data science, and theory/data-driven modeling methods that make digital twins useful in practice. The project-based learning method will be adopted throughout this course. The course will be offered as a combination of lectures and lab sessions, providing an intensive and immersive learning environment. Students will work on challenging projects in groups, which engage them in solving problems and working in teams.