Posts

paper in Architecture Journal

/in , , , /by

Metaverse and Digital Twins in the Age of AI and Extended Reality

Tang, Ming, Mikhail Nikolaenko, Ahmad Alrefai, and Aayush Kumar. 2025. “Metaverse and Digital Twins in the Age of AI and Extended Reality” Architecture 5, no. 2: 36. https://doi.org/10.3390/architecture5020036

This paper explores the evolving relationship between Digital Twins (DT) and the Metaverse, two foundational yet often conflated digital paradigms in digital architecture. While DTs function as mirrored models of real-world systems—integrating IoT, BIM, and real-time analytics to support decision-making—Metaverses are typically fictional, immersive, multi-user environments shaped by social, cultural, and speculative narratives. Through several research projects, the team investigate the divergence between DTs and Metaverses through the lens of their purpose, data structure, immersion, and interactivity, while highlighting areas of convergence driven by emerging technologies in Artificial Intelligence (AI) and Extended Reality (XR).This study aims to investigate the convergence of DTs and the Metaverse in digital architecture, examining how emerging technologies—such as AI, XR, and Large Language Models (LLMs)—are blurring their traditional boundaries. By analyzing their divergent purposes, data structures, and interactivity modes, as well as hybrid applications (e.g., data-integrated virtual environments and AI-driven collaboration), this study seeks to define the opportunities and challenges of this integration for architectural design, decision-making, and immersive user experiences. Our research spans multiple projects utilizing XR and AI to develop DT and the Metaverse. The team assess the capabilities of AI in DT environments, such as reality capture and smart building management. Concurrently, the team evaluates metaverse platforms for online collaboration and architectural education, focusing on features facilitating multi-user engagement. The paper presents evaluations of various virtual environment development pipelines, comparing traditional BIM+IoT workflows with novel approaches such as Gaussian Splatting and generative AI for content creation. The team further explores the integration of Large Language Models (LLMs) in both domains, such as virtual agents or LLM-powered Non-Player-Controlled Characters (NPC), enabling autonomous interaction and enhancing user engagement within spatial environments. Finally, the paper argues that DTs and Metaverse’s once-distinct boundaries are becoming increasingly porous. Hybrid digital spaces—such as virtual buildings with data-integrated twins and immersive, social metaverses—demonstrate this convergence. As digital environments mature, architects are uniquely positioned to shape these dual-purpose ecosystems, leveraging AI, XR, and spatial computing to fuse data-driven models with immersive and user-centered experiences.
 
Keywords:  metaverse; digital twin; extended reality; AI

SMART-DT

/in /by

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.

paper in JMS & NAMRC

/in /by

 

Anuj Gautam, Manish Raj Aryal, Sourabh Deshpande, Shailesh Padalkar, Mikhail Nikolaenko, Ming Tang, Sam Anand, IIoT-enabled digital twin for legacy and smart factory machines with LLM integration, Journal of Manufacturing Systems, Volume 80, 2025, Pages 511-523, ISSN 0278-6125

The paper is also published in the NAMRC 2025 conference.

Anuj Gautam , Manish Raj Aryal, Sourabh Deshpande, Shailesh Padalkar, Mikhail Nikolaenko, Ming Tang, Sam Anand. IIoT-enabled Digital Twin for legacy and smart factory machines with LLM integration. 53rd SME North American Manufacturing Research Conference (NAMRC), Clemson Univ. 06/2025.

 

Abstract

The recent advancement in Large Language Models (LLMs) has significantly transformed the field of natural data interpretation, translation, and user training. However, a notable gap exists when LLMs are tasked to assist with real-time context-sensitive machine data. The paper presents a multi-agent LLM framework capable of accessing and interpreting real-time and historical data through an Industrial Internet of Things (IIoT) platform for evidence-based inferences. The real-time data is acquired from several legacy machine artifacts (such as seven-segment displays, toggle switches, and knobs), smart machines (such as 3D printers), and building data (such as sound sensors and temperature measurement devices) through MTConnect data streaming protocol. Further, a multi-agent LLM framework that consists of four specialized agents – a supervisor agent, a machine-expertise agent, a data visualization agent, and a fault-diagnostic agent is developed for context-specific manufacturing tasks. This LLM framework is then integrated into a digital twin to visualize the unstructured data in real time. The paper also explores how LLM-based digital twins can serve as real time virtual experts through an avatar, minimizing reliance on traditional manuals or supervisor-based expertise. To demonstrate the functionality and effectiveness of this framework, we present a case study consisting of legacy machine artifacts and modern machines. The results highlight the practical application of LLM to assist and infer real-time machine data in a digital twin environment.

Digital Twin of Cincinnati

/in , /by

A realtime flythrough demo for Digital Twin of City Cincinnati

Digital Futures Building at the University of Cincinnati

Destroy Alien buildings near the UC campus. Project developed by students Cooper Pflaum and Nishanth Chidambaram.