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REVIEW article
Front. High Perform. Comput.
Sec. High Performance Big Data Systems
Volume 3 - 2025 |
doi: 10.3389/fhpcp.2025.1536501
This article is part of the Research Topic AI/ML-Enhanced High-Performance Computing Techniques and Runtime Systems for Scientific Image and Dataset Analysis View all articles
A Definition and Taxonomy of Digital Twins: Case Studies with Machine Learning and Scientific Applications
Provisionally accepted- 1 University of California, Merced, Merced, United States
- 2 University of Southern California, Los Angeles, California, United States
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As next-generation scientific instruments and simulations generate ever larger datasets, there is a growing need for high-performance computing (HPC) techniques that can provide timely and accurate analysis. With artificial intelligence (AI) and hardware breakthroughs at the forefront in recent years, interest in using this technology to perform decision-making tasks with continuously evolving real-world datasets has increased. Digital twinning is one method in which virtual replicas of real-world objects are modeled, updated, and interpreted to perform such tasks. However, the interface between AI techniques, digital twins (DT), and HPC technologies has yet to be thoroughly investigated despite the natural synergies between them. This paper explores the interface between digital twins, scientific computing, and machine learning (ML) by presenting a consistent definition for the digital twin, performing a systematic analysis of the literature to build a taxonomy of ML-enhanced digital twins, and discussing case studies from various scientific domains. We identify several promising future research directions, including hybrid assimilation frameworks and physics-informed techniques for improved accuracy.Through this comprehensive analysis, we aim to highlight both the current state-of-the-art and critical paths forward in this rapidly evolving field.
Keywords: Digital Twin, high-performance computing, machine learning, artificial intelligence, World Models
Received: 29 Nov 2024; Accepted: 15 Jan 2025.
Copyright: © 2025 Weingram, Cui, Lin, Munoz, Jacob and Lu. This is an open-access article distributed under the terms of the Creative Commons Attribution License (CC BY). The use, distribution or reproduction in other forums is permitted, provided the original author(s) or licensor are credited and that the original publication in this journal is cited, in accordance with accepted academic practice. No use, distribution or reproduction is permitted which does not comply with these terms.
* Correspondence:
Adam Weingram, University of California, Merced, Merced, United States
Xiaoyi Lu, University of California, Merced, Merced, United States
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