About

Digital Twins

Digital Twins:

Bridging the Physical and Digital Realms.

A digital twin is a dynamic, virtual representation of a physical object or system. It uses real-time data, simulations, and machine learning algorithms to understand, predict, and optimize functions for better performance. From manufacturing equipment to entire cities and even human physiology, digital twins can be applied across various sectors.

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Team Section
Dr. Pu Wang

Dr. Pu Wang

Principal Investigator – Computer Science Professor

Dr. Ahmed Helmy

Dr. Ahmed Helmy

Associate Dean for Research – Computer Science Department

Dr. Liyue Fan

Dr. Liyue Fan

Computer Science Professor

Dr. Srijan Das

Dr. Srijan Das

Computer Science Professor

Choose Us Section

Cutting Edge AI:

Leveraging AI Technologies

Generative Adversarial Networks (GANs):

A class of artificial intelligence algorithms that use two neural networks, one generating data and the other evaluating it, to produce high-quality synthetic data.

Physics-Informed Machine Learning:

Physics-Informed Machine Learning integrates principles of physics into machine learning models, ensuring that predictions adhere to established physical laws. This enhances the accuracy and interpretability of models, especially in science and engineering.

Large Language Models and Assistive Robots:

Advanced AI models trained on vast amounts of text to understand and generate human-like text based on the input. Assistive robots leverage AI to aid in tasks, enhancing healthcare outcomes and providing insights and guidance.