# What is a Digital Twin Anyway? Deriving the Definition for the Built Environment from over 15,000 Scientific Publications

arXiv: https://arxiv.org/abs/2409.19005 · HTML: https://mlsanity.com/paper/2409.19005/

## Key facts

- Cited by 4 papers in the map (data through 14 Oct 2026).
- Builds on 3 of its 136 references that are in the map.
- First posted on arXiv 21 Sep 2024.

## Builds on (most cited first, 3 of 3)

- [Distributed Representations of Sentences and Documents](https://mlsanity.com/paper/1405.4053/) (2014)
- [Personal thermal comfort models using digital twins: Preference prediction with BIM-extracted spatial-temporal proximity data from Build2Vec](https://mlsanity.com/paper/2111.00199/) (2021)
- [Targeting occupant feedback using digital twins: Adaptive spatial-temporal thermal preference sampling to optimize personal comfort models](https://mlsanity.com/paper/2202.10707/) (2022)

## Built on by (most cited first, 4 of 4)

- [Digital Twin Generation from Visual Data: A Survey](https://mlsanity.com/paper/2504.13159/) (2025)
- [Exploring the Potential of Citiverses for Regulatory Learning](https://mlsanity.com/paper/2510.15959/) (2025)
- [Towards a Bathroom-Centered Human-Building Digital Twin Framework for Indoor Safety Analysis](https://mlsanity.com/paper/2606.23292/) (2026)
- [Air Quality Station Simulation via LSTM and Attention-Based Modelling](https://mlsanity.com/paper/2608.11839/) (2026)

## Abstract (arXiv)

The concept of digital twins has attracted significant attention across various domains, particularly within the built environment. However, there is a sheer volume of definitions and the terminological consensus remains out of reach. The lack of a universally accepted definition leads to ambiguities in their conceptualization and implementation, and may cause miscommunication for both researchers and practitioners. We employed Natural Language Processing (NLP) techniques to systematically extract and analyze definitions of digital twins from a corpus of more than 15,000 full-text articles spanning diverse disciplines. The study compares these findings with insights from an expert survey that included 52 experts. The study identifies concurrence on the components that comprise a ``Digital Twin'' from a practical perspective across various domains, contrasting them with those that do not, to identify deviations. We investigate the evolution of digital twin definitions over time and across different scales, including manufacturing, building, and urban/geospatial perspectives. We extracted the main components of Digital Twins using Text Frequency Analysis and N-gram analysis. Subsequently, we identified components that appeared in the literature and conducted a Chi-square test to assess the significance of each component in different domains. Our analysis identified key components of digital twins and revealed significant variations in definitions based on application domains, such as manufacturing, building, and urban contexts. The analysis of DT components reveal two major groups of DT types: High-Performance Real-Time (HPRT) DTs, and Long-Term Decision Support (LTDS) DTs. Contrary to common assumptions, we found that components such as simulation, AI/ML, real-time capabilities, and bi-directional data flow are not yet fully mature in the digital twins of the built environment.