digital twins challenges

Digital twin can be defined as a virtual representation of a physical asset enabled through data and simulators for real-time prediction, optimization, monitoring, controlling, and improved decision making. Recent advances in computational pipelines, multiphysics solvers, artificial intelligence, big data cybernetics, data processing and management

There exist several opportunities and challenges in the methodology and data types underlying digital twins. In diabetes management, the artificial pancreas is a prime example of a digital twin-like system that can greatly improve the ability to monitor and predict blood glucose levels and administer insulin based on non-invasive glucose

To exploit this challenge, the authors propose a unified mathematical foundation that draws from probabilistic graphical models (PGMs) and dynamical system theory to leverage emerging digital twin

Put simply, a digital twin is a virtual replica of a physical object, person, or process that can be used to simulate its behavior to better understand how it works in real life. Digital twins are linked to real data sources from the environment, which means that the twin updates in real time to reflect the original version.

The Digital Twin concept is the latest technology dominating the smart city hype cycle. While the technology has already been around for decades in engineering, thanks to more powerful processing and cloud storage, it has now entered the realm of city planning. The main promise of Digital Twins for local administrations is to make better

Digital Twins (DT) implementation in the Built Environment (BE) industry is still in its early stages. Aiming to increase the knowledge about DT, this study analyzes how DT can be understood in the BE sector and investigates its different potential benefits and expected challenges. To do so, the Systematic Literature Review (SLR) approach was

A digital twin is a virtual representation of a physical object or process capable of collecting information from the real environment to represent, validate and simulate the physical twin''s present and future behavior. It is a key enabler of data-driven decision making, complex systems monitoring, product validation and simulation and

Digital twin challenges CISOs face. By using data from a digital twin, a real-world device or system can be adjusted to work as efficiently as possible for cost savings and to extend its life

These challenges may be solved by using technologies that can be edge intelligence, which integrates edge computing with AI, providing low-latency and high-security computing services and improving the processes'' performance. 5. Trends and Challenges of the Digital Twin.

Challenges in digital twin lifecycle technology. As discussed beforehand, there are many engineering challenges in putting Digital Twin into practice. To start solving all these challenges there is a need for technological infrastructure and knowledge build on the Digital Twin paradigm to bring Digital Twin Applications to fruition.

Such customisation will become possible through the emergence of digital twin (DT) technology. The objective of this article is to review the progress of prominent research on DT technology in medicine and discuss the potential applications and future opportunities as well as several challenges remaining in digital healthcare.

Digital Twins are virtual copies of products, processes or services which encompass all the above qualities [1]. Grieves and Vickers [2] define the Digital Twin (DT) as "a set of virtual information constructs that fully describes a potential or actual physical manufactured product from the micro atomic level to the macro geometrical level

ABSTRACT. Digital twins enjoy increasing interest in a diverse array of industrial sectors, such as manufacturing, healthcare, urban planning, etc. Their usefulness depends on the robustness of

The danger of inaccurately representing an object using digital twin. The biggest concern most business owners who are interested in this technology have is the risk of misrepresenting the object or system they want to replicate using this technology. Considering the fact that there is not much information regarding how accurate the twin is

Opportunities and Challenges for Digital Twins in Engineering: Proceedings of a Workshop—in Brief The digital twin is an emerging technology that builds on the convergence of computer science, mathematics, engineering, and the life sciences. Digital twins have potential across engineering domains, from aeronautics to renewable energy.

A digital twin may pose challenges related to healthcare, especially in the case where bi-directional communication capabilities are implemented. 3 Digital Twins in Healthcare In the healthcare sector, the digital twin has the potential to be used for several different purposes, such as precision medicine [ 7, 8, 10, 12, 42 ], remote care

A digital twin (DT), as part of a DT system, is a virtual representation of an entity, process, or system, synchronized at a frequency and fidelity sufficient to maintain state concurrence. TLR-RES/DE/REB-2021-17 "Technical Challenges and Gaps in Digital-Twin-Enabling Technologies for Nuclear Reactor Application" December 2021.

The application of digital twins in industry has become increasingly common, but not without important challenges to be addressed by the research community.

Digital twins can iterate and evolve through the seamless connection and fusion between the virtual and physical spaces1, and this consistency and synchronization can bring many benefits to numer

The areas of highest security importance include: 2. Manage your data quality. Digital twin models depend on the data from thousands of remote sensors communicating over unreliable networks. Companies that want to implement digital twin technology must be able to exclude bad data and manage gaps in the data streams. 3.

We reviewed the documents to find answers to the following questions: What is a Digital Twin, what technologies are used, what are the current applications of Digital Twins in different industries, and what are the challenges and opportunities

In the rapidly evolving landscape of Industry 4.0, digital twins have emerged as a transformative technology across various industrial sectors. This paper presents a comprehensive, in-depth review of digital twin models in terms of the concept and evolution, fundamental components and frameworks, and existing digital twin models

The main challenge of constructing spatial digital twins is the immense amount of data required to describe, for example, an entire city, when using standard geometrical representation approaches; taking further into account the additional value added by automated semantic description of sensor data, due to the availability of high

Here we provide a brief overview of digital twin advancements in industry and highlight the main pitfalls to avoid and challenges to overcome, to improve the

The results indicate that technical and non-technical issues challenge digital twins in the urban and geospatial spatial domains to different degrees. Some challenges, e.g. definitions and purposes, are fundamental issues that need to be clarified at the beginning. Although current research has defined urban digital twins in many

Digital Twin (DT) is an emerging technology surrounded by many promises, and potentials to reshape the future of industries and society overall. A DT is a system-of-systems which goes far beyond the traditional computer-based simulations and analysis. It is a replication of all the elements, processes, dynamics, and firmware of a