Digital Twins: Bones as Flesh: Redrawing territories through indeterminate bodies

The time needed to eliminate complications and accidents accounts for 20–25% of total well construction time, according to a 2020 SPE paper (SPE 200740). The same paper notes that digital twins have proven to be a key enabler in improving sustainability during well construction, shrinking the carbon footprint by reducing overall drilling time and encouraging and bringing confidence to contactless advisory and collaboration. The paper also points out the potential application of digital twins to activities such as geothermal drilling. Advanced data analytics and machine learning (ML) potentially can reduce engineering hours up to 70% during field development, according to Boston Consulting Group. Increased field automation, remote operations, sensor costs, digital twins, machine learning, and improved computational speed are responsible. It is no surprise, then, that digital twins are taking on a greater sense of urgency for operators, service companies, and drilling contractors working to improve asset and enterprise safety, productivity, and performance management. For 2021, digital twins appear among the oil and gas industry’s top 10 digital spending priorities. DNV GL said in its Technology Outlook 2030 that this could be the decade when cloud computing and advanced simulation see virtual system testing, virtual/augmented reality, and machine learning progressively merge into full digital twins that combine data analytics, real-time, and near-real-time data for installations, subsurface geology, and reservoirs to bring about significant advancements in upstream asset performance, safety, and profitability. The biggest challenges to these advancements, according to the firm, will be establishing confidence in the data and computational models that a digital twin uses and user organizations’ readiness to work with and evolve alongside the digital twin. JPT looked at publications from inside and outside the upstream industry and at several recent SPE papers to get a snapshot of where the industry stands regarding uptake of digital twins in well construction and how the technology is affecting operations and outcomes. Why Digital Twins Gartner Information defines a digital twin as a digital representation of a real-world entity or system. “The implementation of a digital twin,” Gartner writes, “is an encapsulated software object or model that mirrors a unique physical object, process, organization, person or other abstraction.” Data from multiple digital twins can be aggregated for a composite view across several real-world entities and their related processes. In upstream oil and gas, digital twins focus on the well—and, ultimately, the field—and its lifecycle. Unlike a digital simulation, which produces scenarios based on what could happen in the physical world but whose scenarios may not be actionable, a digital twin represents actual events from the physical world, making it possible to visualize and understand real-life scenarios to make better decisions. Digital well construction twins can pertain to single assets or processes and to the reservoir/subsurface or the surface. Ultimately, when process and asset sub-twins are connected, the result is an integrated digital twin of the entire asset or well. Massive sensor technology and the ability to store and handle huge amounts of data from the asset will enable the full digital twin to age throughout the life-cycle of the asset, along with the asset itself (Fig. 1).

While the field of hydrography is crucial for maritime navigation and other maritime applications, oceanography is the field that provides the relevant data and knowledge for predicting climate change, monitoring marine resources, and exploring marine life. Digital ocean twins combine these two exciting fields and combine ocean observations and ocean models to establish virtual representations of a real world system, in this case the ocean or an ocean area, as well as assets in the ocean and processes within ocean industries or the natural environment. They have the potential to play a critical role in optimising and supporting sustainable ocean development. Digital Twins are synchronised with their real-world counterparts at a specific frequency and fidelity. They can provide valuable insights into the ocean's state and its evolution over time, which can be used to support decision-making in ocean governance and various ocean-related industries. Digital ocean twins can transform human ocean interactions by accelerating holistic understanding, optimal decision-making, and effective interventions. Digital twins of the ocean use ocean observations, historical and forecast data to represent the past and present and simulate possible future scenarios. They are motivated by outcomes, tailored to use cases, powered by integration, built on data, guided by domain knowledge, and implemented in IT systems. In this article, we explore the benefits of digital twins for the ocean, the challenges in developing them, and the current state of the art in ocean digital twin technology. One of the main benefits of digital ocean twins is their ability to provide accurate predictions of ocean conditions under expected interventions. Their information can be used to support decision- making in various applications including ocean-related industries, such as fishing, shipping, and offshore energy production. Additionally, digital twins can help to improve our understanding of the ocean's complex processes and their interactions with human activities, such as climate change, pollution, resource extraction and overfishing. Researchers and IT companies are combining various technologies and data sources, such as the Internet of Things for ocean observations, state of the art data science, artificial intelligence and machine learning, data spaces and vocabularies into digital ocean twins to contextualise data, improve the accuracy of ocean models and make ocean knowledge more accessible to a wide range of users.

Background: The growing availability of high-resolution imaging, biosensors, molecular profiling, and artificial intelligence has enabled the development of digital patient twins—computational models that reproduce individual physiological and pathological processes in silico. While digital twins have been widely proposed as tools for personalised medicine, their clinical and translational value across major disease domains has not yet been systematically synthesised. Methods: A narrative review was conducted of full-text publications from 2020–2025 addressing digital patient twins in cardiology, oncology, chronic disease management, and rehabilitation. The analysed literature included translational and clinical studies, mechanistic modelling papers, and healthcare system implementations. Evidence was prioritised from studies reporting patient-specific simulations, comparisons with real clinical or imaging data, and therapy-support scenarios. Results: In cardiology, electrophysiological and haemodynamic digital twins demonstrated high concordance with invasive mapping and imaging data and were associated with improved ablation planning, device optimisation, and reduced arrhythmia recurrence. In oncology, tumour digital twins integrating imaging and molecular data predicted tumour growth and treatment response with clinically meaningful accuracy, supporting personalised and adaptive cancer therapy. In chronic diseases, sensor-driven digital twins enabled early detection of physiological deterioration and supported proactive intervention, reducing exacerbations and hospitalisations. In rehabilitation, biomechanical and neurophysiological digital twins improved functional recovery by guiding personalised and robot-assisted therapy. Conclusions: Digital patient twins are transitioning from experimental computational tools to clinically relevant systems capable of influencing diagnosis, therapy selection, monitoring, and patient outcomes. By enabling in silico testing of therapeutic strategies on a virtual representation of the patient, digital twins reduce uncertainty in clinical decision-making and support truly personalised care. Continued progress in data integration, model validation, and regulatory governance will be essential for their safe and widespread adoption in clinical practice.

Digital twin for sustainable manufacturing supply chains: Current trends, future perspectives, and an implementation framework

As health-care budgets are continuously under increasing demands, Artificial Intelligence resources such as digital heart twins could save millions of dollars by predicting results and preventing unnecessary surgery. Can we start to make digital human body twins to plant and predict health outcomes for a patient? By using a way to design competent simulation models from real objects, digital twins were created through IoT. But the digital twin is a complicated system and a very long-drawn step away from its possibilities. Researchers must design all components of entities or structures. There is a need to collect and merge various types of data. Many engineering researchers and participants aren’t sure about which technologies and resources to use. The 3D digital twin model offers a reference guide for digital twin comprehension and implementation. This paper aims to investigate and outline the recent technologies and tools used for digital twin applications from a 3-D digital model perspective, such as references to technologies and tools for future digital twin applications.

This chapter presents a Digital Twin Pipeline Framework of the COGNITWIN project that supports Hybrid and Cognitive Digital Twins, through four Big Data and AI pipeline steps adapted for Digital Twins. The pipeline steps are Data Acquisition, Data Representation, AI/Machine learning, and Visualisation and Control. Big Data and AI Technology selections of the Digital Twin system are related to the different technology areas in the BDV Reference Model. A Hybrid Digital Twin is defined as a combination of a data-driven Digital Twin with First-order Physical models. The chapter illustrates the use of a Hybrid Digital Twin approach by describing an application example of Spiral Welded Steel Industrial Machinery maintenance, with a focus on the Digital Twin support for Predictive Maintenance. A further extension is in progress to support Cognitive Digital Twins includes support for learning, understanding, and planning, including the use of domain and human knowledge. By using digital, hybrid, and cognitive twins, the project’s presented pilot aims to reduce energy consumption and average duration of machine downtimes. Data-driven artificial intelligence methods and predictive analytics models that are deployed in the Digital Twin pipeline have been detailed with a focus on decreasing the machinery’s unplanned downtime. We conclude that the presented pipeline can be used for similar cases in the process industry.

Currently, the main problem highlighted by specialists during digital twins testing relates to ensuring the test results reliability. The aim of this article is to assess the uncertainty of testing digital product twins for compliance with electromagnetic compatibility standards. In this regard, the authors developed an automated system model for testing digital product twins for compliance with electromagnetic compatibility standards. This model is applicable to digital twins of any electronic products that are capable of creating electromagnetic interference. It can be also used in cases when the quality of operation depends on the effect of external electromagnetic interference. Real system simplifications and abstractions are used in modelling, and therefore it is necessary to ensure that the results of the simulation can be used in decision-making on the compliance of products with EMC requirements. The analysis of the fundamental works on assessing of testing product digital twins uncertainty allowed to estimate the most probable budget of uncertainties that can affect the result reliability of an automated system for testing digital product twins for compliance with electromagnetic compatibility standards. Uncertainty assessment was carried out using the methods proposed in the basic international EMC standards (CISPR 16-4-2: 2011, ISO / IEC 17025). The uncertainty estimation algorithm proposed by the authors can be applied by product manufacturers that strive to increase the test results reliability of digital twins for EMC. This is of particular importance to solve technical security problems in areas such as power industry, electronics, transport security systems and weapons.KeywordsDigital twinsElectromagnetic interference emissionTest reliabilityDigital test model

SUMMARYWhile the definition of a Digital Twin (DT) is provided within recent literature, each DT should be designed to meet the specific requirements of the user. As RAMS is focused on the identification, understanding and mitigation of technical risk in a system, a RAMS engineer requires a DT that can autonomously establish the potential dependencies and impacts of functional and physical failures on a system, and auto-generate various analyses to identify the appropriate mitigation approach.The concept of a Digital Risk Twin (DRT) described in this paper should uses an integrated and inter-related set of information about the system, autonomously reflecting changes across analyses that utilize causal simulation to understand potential risks and map their dependencies. This definition has been examined thoroughly from the original literature for Digital Twins, through an examination of core system safety/risk assessment practices demonstrated in RAMS and finally arrives at the main point of the definition and key aspects of a Digital Risk Twin (DRT).As the DRT digitizes the RAMS process across each stage of the Product Lifecycle, it is important that it offers common DT features such as integration, visualization, and simulation. The DRT will also implicitly digitize the engineering domain knowledge utilized in the design process (‘Digital Domain Knowledge’), providing a persistent context for analysis and design decisions, and so requires a framework of automated data management, maintaining traceability between activities and decisions made in relation to identified risks.

Sustained continuous monitoring and replication of organizational development in digital organizational twins is of particular importance for labour-intensive enterprises and also those in which reciprocal relations between social, corporate, normative and performative aspects assume the leading role. The main purpose of the research is the developing of a digital representation of organizational processes, which focuses on the performance, working activities, organizational issues, behaviour and interactions between of the organizational members. Consequently, the objectives of research include the monitoring of current research state, concept and design of a digital twin. The implementation of digital organizational twin should improve considering timely optimization of proactive and reactive organizational development measures in the company in relation to the core variables of the 7S model. The created digital twin should map the dynamics of organizational development, as well as concomitant and deviating processes. Systematization literary sources and approaches for the digital replication of organizational development issues indicates the lack of publications on research and diffuse distribution of scientific interest. The initial design of organizational development in the digital twin is based on four main objects and limited to a certain number of investigated parameters. This paper compare the conventional and digitalized organizational development process, explain the data flow in digital organizational twin, the design of organizational development in the digital organizational twin, provide an overview of the individual facets of organizational development, list the parameterization models and exemplarily illustrate the visualization of selected parameters. The results of the research can be useful for the expansion of the tension bridge between organisational development and technologies and the development of new potentials for the study of socio-technical effects in companies. This can be extended to include the other facets of business management and supplemented by the connection of other technological resources.

ABSTRACTIn precision medicine, digital twins—virtual models of patients created using personalized data and advanced machine learning—are potentially changing healthcare by predicting health outcomes and guiding medical decisions. However, their use raises complex ethical questions, particularly concerning their relationship to human dignity. Patients often regard dignity as central to their healthcare experience, and failing to incorporate this principle into the design and application of digital twins risks undermining personal autonomy, misusing sensitive data, eroding patient–provider trust, and creating broader ethical challenges. This paper argues that digital twins are not mere data sets or predictive tools, but symbolic extensions of the dignity of the individuals they represent. Using David Kirchhoffer's multidimensional framework of human dignity, this study examines how digital twins engage with both absolute (inherent) and contingent (socially constructed) dimensions of dignity. The analysis begins by exploring the multidimensional concept of human dignity, followed by a discussion of how digital twins embody these dimensions, illustrated through examples such as digital brain twins, posthumous digital representations, and disability contexts. Finally, the paper addresses the ethical implications of these findings, emphasizing the moral responsibilities of researchers, developers, and clinicians to treat digital twins as representations of patient dignity, thereby ensuring these technologies advance healthcare without compromising the fundamental respect owed to every individual.

'Digital twins', also called 'digital patient twins' or 'virtual human twins' - digital patient-specific models derived from multimodal health data - are a strong focus in health care and are emerging as a promising tool for improving personalized care in uro-oncology. These models can integrate clinical, genomic, imaging and histopathological information to simulate organ behaviour and disease progress as well as predict responses to treatments. The concept of digital twins has shown potential in various fields, but its application in uro-oncology is still evolving, with few assessments of their feasibility and clinical utility. The advent of artificial intelligence adds a new dimension to their development, potentially enabling the synthesis of diverse, high-quality datasets to improve modelling accuracy and support real-time decision-making. However, substantial challenges exist, including data integration, patient privacy, computational demands and ethical frameworks. In addition, the interpretability of predictions remains essential for gaining clinical trust and guiding patient-centred decisions. The use of digital twins in uro-oncology has the potential to improve patient stratification and treatment planning; however, barriers must be overcome for their successful implementation in clinical routine. By integrating new technologies, fostering interdisciplinary collaboration and prioritizing transparency, digital twins could shape the future of precision uro-oncology.

Digital twins are virtual models of physical artefacts that may or may not be synchronously connected, and that can be used to simulate their behavior. They are widely used in several domains such as manufacturing and automotive to enable achieving specific quality goals. In the health domain, so-called digital patient twins have been understood as virtual models of patients generated from population data and/or patient data, including, for example, real-time feedback from wearables. Along with the growing impact of data science technologies like artificial intelligence, novel health data ecosystems centered around digital patient twins could be developed. This paves the way for improved health monitoring and facilitation of personalized therapeutics based on management, analysis, and interpretation of medical data via digital patient twins. The utility and feasibility of digital patient twins in routine medical processes are still limited, despite practical endeavors to create digital twins of physiological functions, single organs, or holistic models. Moreover, reliable simulations for the prediction of individual drug responses are still missing. However, these simulations would be one important milestone for truly personalized therapeutics. Another prerequisite for this would be individualized pharmaceutical manufacturing with subsequent obstacles, such as low automation, scalability, and therefore high costs. Additionally, regulatory challenges must be met thus calling for more digitalization in this area. Therefore, this narrative mini-review provides a discussion on the potentials and limitations of digital patient twins, focusing on their potential bridging function for personalized therapeutics and an individualized pharmaceutical manufacturing while also looking at the regulatory impacts.

Digital Energy Twins are IT systems, interconnecting sensor data, simulation models and user interfaces to formulate a virtual representation of the behavior of real energy systems. Digital Energy Twins are useful to predict the behavior of energy systems under varying boundary conditions and to optimize their operation considering economic and ecologic impact. Two different concepts of Digital Twins applicable to industrial energy systems were demonstrated: Digital Energy Twins and Digital Energy Shadows. While in literature, the term “Digital Twin” is widely used as synonym, for rather different applications involving simulations and virtual models connected to real-world data, this paper elaborates on the differences between digital twins and digital shadows in more detail. Given by the complexity of real-world energy systems (heat and electricity) and their implications on real time simulation, the concepts are demonstrated on different TRL levels. The results show the benefits and limitations of Digital Energy Twin and Digital Energy Shadow applications in relevant environments.

A pipeline for developing digital cardiac twins integrating cardiovascular magnetic resonance and electrocardiographic imaging: results from the MyoFit46 study

Over the last few years, a concept called Digital Twin has evolved rapidly as a new key approach in the field of Product Lifecycle Management (PLM). Briefly, a Digital Twin is a digital representation of an active unique product or unique product-service-system with its selected characteristics within dedicated lifecycle phases. This concept has experienced a tremendous impact by IoT technology, which has drastically reduced the costs. It builds the foundation not only for connected products and services but also for entirely new offerings and business models. Three main characteristics of Digital Twin were identified: representation of a physical system, bidirectional data exchange, and the connection along the entire lifecycle. For a better understanding, three subtypes of Digital Twin are presented, namely: The Digital Master, the Digital Manufacturing Twin, and the Digital Instance Twin which refer to the different phases of the product lifecycle: design, production and operation. Therefore, this chapter formulates a consistent and detailed definition of Digital Twins and gives insight in dedicated research direction. Finally, based on the Digital Twin characteristics, an approach for generation of Digital Twin in manufacturing is shown.