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SMART-DT

SMART-DT: Scalable Multi-Agent Reinforcement Learning and Collaborative AI for Digital Twin Platform of Infrastructure and Facility Operations.

Principal Investigators:

  • Prof. Sam Anand, Department of Mechanical Engineering, CEAS
  • Prof. Ming Tang, Extended Reality Lab, Digital Futures, DAAP

Grant: $40,000. UC Industry 4.0/5.0 Institute Consortium Research Project: 03.2025-01.2026

In this phase, we will develop a scalable digital twin that integrates machine, factory, and city-level data with AI-driven real-time decision-making. The key questions we aim to answer are:

  • Can a high-fidelity Digital Twin (DT) be efficiently built using only image and video data?
  • How can multiple specialized Large Language Model (LLM) agents—at machine, factory, and city levels—collaborate to generate relevant insights?
  • How effective is synthetic data from a Digital Twin for object detection and process recognition?
  • Does combining traditional Machine Learning (ML) with Large Language Models (LLMs) improve decision-making in complex manufacturing operations?

The project’s primary goal is to create a scalable, cloud-based digital twin that enhances operational efficiency through AI-driven insights. Additional technical objectives include:

  • Using advanced reality capture techniques (e.g., Gaussian Splatting) to build a Digital Twin from images and videos and simulate fault scenarios at factory and data center levels.
  • Integrating an IIoT data framework to track material flow, process handling, operational metrics, and equipment status for seamless cloud-based analysis.
  • Developing a synthetic data capture system using a simulated drone within the Digital Twin to train reinforcement learning models for fault prediction.
  • Designing a multi-agent AI system combining LLMs, machine learning, and reinforcement learning to enable dynamic communication, prediction, and diagnostics in the factory.

 

last year’s project: IIOT for legacy and intelligent factory machines with XR and LLM feedback with a Digital Twin demonstration of real-time IOT for architecture/building applications using Omniverse.

o4a AAA Partnership Award

2022 Outstanding AAA Partnership Award of the Year

 

On behalf of COA and Live Well, Ken Wilson (COA) and Ming Tang (UC)  received the AAA Award at the o4a conference. 10.20.2022. It is my great honor to represent Live Well as the co-recipient with the Council on Aging to receive the 2022 Ohio Association of Area Agencies on Aging Annual Partnership Award. Thanks to Suzanne Burke, Ken Wilson, Jai’La Nored, Anna Goubeaux, and many others from COA. Thanks to the Live Well EVRTalk development team (Faculty: Ming Tang, Matt Anthony; advisor: Craig Vogel, Linda Dunseath; Students and Live Well fellows: Tosha Bapat, Karly Camerer, Jay Heyne, Harper Lamb, Jordan Owens, Ruby Qji, Alejandro Robledo, Matthew Spoleti, Lauren Southwood, Ryan Tinney, Keeton Yost, Dongrui Zhu.)

Link: LWC Twitter

2020 ASIAN DESIGN AWARD

The Tiniest Cottage design-build project by the University of Cincinnati and Beijing Jiaotong University won second place in the “Ecological Healthy and Sustainable Design” section at the 2020 ASIAN DESIGN AWARD

  • UC Students: Lauren Figley, Jordan Micham, Pat McQuillen, Vu Tran, Jeremy Swafford, Tess Ryan.
  • BJTU students: Zhuo Chen, Peida Zhuang, Shurui Li, Zhixuan Li, Yingjie Liu, Zijia Wang, Yuanjia Luo, Wenjun Lin, Yanqi Yi
  • Faculty supervisor:  Whitney Hamaker, Ming Tang (UC); Yingdong Hu, Yunan Zhang, Yongquan Chen (BJTU)

2020 ADA Award Theme: Towards the social design

Social design is the design that is mindful of the designer’s role and responsibility in society, and of the use of the design process to bring about social change. Social design is also a critical discipline that challenges the pure market-orienteers of conventional design practice and attempts to see past this into a more inclusive conception of design, in which user groups who are marginalized are also given priority.