# The Digital Twin Landscape at the Crossroads of Predictive Maintenance, Machine Learning and Physics Based Modeling

arXiv: https://arxiv.org/abs/2206.10462 · HTML: https://mlsanity.com/paper/2206.10462/

## Key facts

- Cited by 4 papers in the map (data through 14 Oct 2026).
- Builds on 7 of its 81 references that are in the map.
- First posted on arXiv 21 Jun 2022.

## Builds on (most cited first, 7 of 7)

- [Neural Ordinary Differential Equations](https://mlsanity.com/paper/1806.07366/) (2018)
- [Digital Twin: Values, Challenges and Enablers](https://mlsanity.com/paper/1910.01719/) (2019)
- [Physics Guided RNNs for Modeling Dynamical Systems: A Case Study in Simulating Lake Temperature Profiles](https://mlsanity.com/paper/1810.13075/) (2018)
- [Fully convolutional networks for structural health monitoring through multivariate time series classification](https://mlsanity.com/paper/2002.07032/) (2020)
- [Graph representation forecasting of patient's medical conditions: towards a digital twin](https://mlsanity.com/paper/2009.08299/) (2020)
- [System-Level Predictive Maintenance: Review of Research Literature and Gap Analysis](https://mlsanity.com/paper/2005.05239/) (2020)
- [Evidence-based Prescriptive Analytics, CAUSAL Digital Twin and a Learning Estimation Algorithm](https://mlsanity.com/paper/2104.05828/) (2021)

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

- [State-of-the-art review and synthesis: A requirement-based roadmap for standardized predictive maintenance automation using digital twin technologies](https://mlsanity.com/paper/2311.06993/) (2023)
- [State-of-the-Art Review: The Use of Digital Twins to Support Artificial Intelligence-Guided Predictive Maintenance](https://mlsanity.com/paper/2406.13117/) (2024)
- [FastSurvival: Hidden Computational Blessings in Training Cox Proportional Hazards Models](https://mlsanity.com/paper/2410.19081/) (2024)
- [Grounding Time-Series Foundation Models in Digital Twin Topology for Predictive Maintenance](https://mlsanity.com/paper/2609.40071/) (2026)

## Abstract (arXiv)

The concept of a digital twin has exploded in popularity over the past decade, yet confusion around its plurality of definitions, its novelty as a new technology, and its practical applicability still exists, all despite numerous reviews, surveys, and press releases. The history of the term digital twin is explored, as well as its initial context in the fields of product life cycle management, asset maintenance, and equipment fleet management, operations, and planning. A definition for a minimally viable framework to utilize a digital twin is also provided based on seven essential elements. A brief tour through DT applications and industries where DT methods are employed is also outlined. The application of a digital twin framework is highlighted in the field of predictive maintenance, and its extensions utilizing machine learning and physics based modeling. Employing the combination of machine learning and physics based modeling to form hybrid digital twin frameworks, may synergistically alleviate the shortcomings of each method when used in isolation. Key challenges of implementing digital twin models in practice are additionally discussed. As digital twin technology experiences rapid growth and as it matures, its great promise to substantially enhance tools and solutions for intelligent upkeep of complex equipment, are expected to materialize.