mlsanityData to 7 Oct 2026

Digital Twins in Wind Energy: Emerging Technologies and Industry-Informed Future Directions

arXiv 2304.11405 ↗First posted 16 Apr 2023cs.HC cs.LGcited by 7

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Abstract (arXiv)

This article presents a comprehensive overview of the digital twin technology and its capability levels, with a specific focus on its applications in the wind energy industry. It consolidates the definitions of digital twin and its capability levels on a scale from 0-5; 0-standalone, 1-descriptive, 2-diagnostic, 3-predictive, 4-prescriptive, 5-autonomous. It then, from an industrial perspective, identifies the current state of the art and research needs in the wind energy sector. The article proposes approaches to the identified challenges from the perspective of research institutes and offers a set of recommendations for diverse stakeholders to facilitate the acceptance of the technology. The contribution of this article lies in its synthesis of the current state of knowledge and its identification of future research needs and challenges from an industry perspective, ultimately providing a roadmap for future research and development in the field of digital twin and its applications in the wind energy industry.

Key facts. Cited by 7 papers in the map (data through 7 Oct 2026). Builds on 22 of its 204 references that are in the map. First posted on arXiv 16 Apr 2023.

Builds on

22 of 204 references.

References · 22

  1. 2022Scientific Machine Learning through Physics-Informed Neural Networks: Where we are and What's nextcited by 603
  2. 2018Threat of Adversarial Attacks on Deep Learning in Computer Vision: A Surveycited by 459
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  5. 2016Theory-guided Data Science: A New Paradigm for Scientific Discovery from Datacited by 169
  6. 2020Integrating Scientific Knowledge with Machine Learning for Engineering and Environmental Systemscited by 160
  7. 2019Digital Twin: Values, Challenges and Enablerscited by 81
  8. 2022Theseus: A Library for Differentiable Nonlinear Optimizationcited by 64
  9. 2020A Probabilistic Graphical Model Foundation for Enabling Predictive Digital Twins at Scalecited by 58
  10. 2020A Physics-Informed Machine Learning Approach for Solving Heat Transfer Equation in Advanced Manufacturing and Engineering Applicationscited by 53
  11. 2020Combining data assimilation and machine learning to infer unresolved scale parametrisationcited by 40
  12. 2020Operator inference for non-intrusive model reduction of systems with non-polynomial nonlinear termscited by 32
10 more
  1. 2020Physics guided machine learning using simplified theoriescited by 23
  2. 2019Data-Driven POD-Galerkin Reduced Order Model for Turbulent Flowscited by 21
  3. 2022Physics guided neural networks for modelling of non-linear dynamicscited by 20
  4. 2021Deep neural network enabled corrective source term approach to hybrid analysis and modelingcited by 16
  5. 2021Hybrid analysis and modeling, eclecticism, and multifidelity computing toward digital twin revolutioncited by 14
  6. 2021Nonlinear proper orthogonal decomposition for convection-dominated flowscited by 11
  7. 2022Collective wind farm operation based on a predictive model increases utility-scale energy productioncited by 6
  8. 2022Artificial intelligence-driven digital twin of a modern house demonstrated in virtual realitycited by 4
  9. 2023Demonstration of a Standalone, Descriptive, and Predictive Digital Twin of a Floating Offshore Wind Turbinecited by 3
  10. 2016Variable Transmission Voltage for Loss Minimization in Long Offshore Wind Farm AC Export Cablescited by 1

Built on by

Cited by year

Citing papers per year: 2023: 1; 2024: 2; 2025: 4.

7 papers.

  1. 2025Method of Manufactured Learning for Solver-free Training of Neural Operatorscited by 3
  2. 2024Predictive Digital Twin for Condition Monitoring Using Thermal Imagingcited by 2
  3. 2025PINN-DT: Optimizing Energy Consumption in Smart Building Using Hybrid Physics-Informed Neural Networks and Digital Twin Framework with Blockchain Securitycited by 2
  4. 2024Diagnostic Digital Twin for Anomaly Detection in Floating Offshore Wind Energycited by 1
  5. 2025Toward a digital twin of U.S. Congresscited by 1
  6. 2025Hybrid Modeling, Sim-to-Real Reinforcement Learning, and Large Language Model Driven Control for Digital Twinscited by 1
  7. 2023Enhancing wind field resolution in complex terrain through a knowledge-driven machine learning approachcited by 0