Digital twins have rapidly moved from a conceptual innovation to a practical engineering tool that is reshaping how we design, operate and optimise complex systems. From aerospace and manufacturing to infrastructure and media, the ability to create a dynamic, data-driven virtual representation of real-world assets is opening up new possibilities for insight, efficiency and resilience.
On 24 June 2026, the IET will bring this conversation to the forefront with the webinar Digital Twin Dynamics: A Multi-Disciplinary Debate. This online event gathers voices from across several Technical Networks to explore not just what digital twins are, but how they are evolving and where they may lead next.
This blog offers a preview of that discussion, highlighting both the technical foundations of digital twins and the value of a cross-sector perspective.
What is a Digital Twin, Really?
At its core, a digital twin is a virtual representation of a physical system that is continuously updated with real-world data. Unlike traditional simulation models, which are often static or scenario-based, digital twins are dynamic. They evolve alongside the system they represent, incorporating sensor data, operational inputs and contextual factors in near real time.
Technically, a robust digital twin brings together several key components:
- Data acquisition from sensors and IoT devices
- Data integration and storage, often through cloud or edge platforms
- Mathematical and computational models, including physics-based and data-driven approaches
- Analytics and visualisation tools that enable interpretation and decision-making
Increasingly, digital twins also integrate machine learning models to move beyond descriptive insights toward predictive and prescriptive capabilities.
The Rise of AI-Enabled Digital Twins
One of the most significant developments in recent years has been the integration of artificial intelligence into digital twin ecosystems. This combination enables systems to not only mirror physical processes but also learn from them.
For example, in advanced manufacturing, AI-driven twins can identify inefficiencies, predict equipment failure and optimise production schedules. Dr Jon Stammers, who leads research at the University of Sheffield’s Advanced Manufacturing Research Centre, focuses on embedding data and analytics directly into manufacturing workflows. His work demonstrates how digital twins can transition from monitoring tools to active participants in decision-making.
Similarly, in critical infrastructure and safety-critical environments, explainable AI is becoming essential. Dr Zhouxiang Fei’s expertise in predictive modelling and anomaly detection highlights the need for transparency and trust in AI systems that operate within digital twins. Particularly in sectors like nuclear energy or transport, understanding why a model makes a prediction is just as important as the prediction itself.
Managing Complexity and Uncertainty
Digital twins are especially valuable in systems where complexity and uncertainty are major challenges. Marc Thomas, Digital Twin Lead at NATS, works on modelling UK airspace through a probabilistic digital twin. In this context, uncertainty is not a flaw to eliminate but a factor to model explicitly.
By incorporating probabilistic methods, high-fidelity simulations and real-world traffic data, digital twins can evaluate how AI systems perform under realistic conditions. This approach is critical in areas such as air traffic control, where safety margins are tight and decision-making must account for countless variables.
This highlights an important shift in engineering practice. Digital twins are not just about replicating reality but about exploring possible futures under varying conditions.
Cross-Sector Applications: A Shared Challenge
One of the defining features of digital twin technology is its broad applicability. Yet each sector brings its own challenges, constraints and opportunities.
- Aerospace and transport demand high fidelity models and rigorous validation
- Manufacturing focuses on efficiency, adaptability and integration
- Media and entertainment explore digital twins for content delivery and user experience
- Energy and infrastructure prioritise resilience, sustainability and long-term planning
Professor John Easton’s work on digital twins for sustainable transport illustrates how these models can support decarbonisation strategies. By simulating different policy and infrastructure scenarios, digital twins can act as decision support tools for cities aiming to reduce emissions.
Meanwhile, innovation leaders like Mark Scibor-Rylski bring a commercial perspective, emphasising how digital twins can de-risk early-stage technologies and accelerate investment in emerging solutions.
The Human and Organisational Layer
While the technical architecture of digital twins is critical, their success ultimately depends on how organisations adopt and use them.
Digital twins require collaboration across disciplines. Engineers, data scientists, software developers and domain experts must work together to build models that are both technically sound and contextually relevant. This is reflected in the diverse backgrounds of the event speakers, from particle accelerators and AI platforms to venture capital and media systems.
Adnan Khattak’s experience in medical technology highlights the importance of domain knowledge in shaping effective digital twins. In highly specialised fields, a purely data-driven approach is rarely sufficient. Instead, domain expertise guides model design, validation and interpretation.
Similarly, full stack developers like Oluwatobi Ajayi are essential in translating complex models into scalable, enterprise-ready solutions. Building the pipelines, interfaces and platforms that support digital twins is a major engineering challenge in itself.
Why a Multi-Disciplinary Debate Matters
Given the diversity of applications and approaches, it is clear that no single discipline can fully define the future of digital twins. This makes a multi-disciplinary debate not only valuable but necessary.
The IET webinar will bring together perspectives from networks including Aerospace, AI, Manufacturing, Media and Railway Systems. This format encourages dialogue rather than one-way presentation, allowing for contrasting views and shared learning.
Attendees can expect insights into:
- Emerging trends and technical challenges
- The role of AI and data in shaping next-generation twins
- Practical case studies across industries
- The balance between innovation and risk in adopting digital twins
Importantly, the event is designed to be engaging and accessible, with a dynamic format that supports global participation.
Continuing Professional Development Opportunity
Beyond the insights, the event also contributes to Continuing Professional Development. It offers a chance for engineers and technologists to stay current with rapidly evolving tools and methodologies, while engaging with a broader professional community.
As digital twins become more embedded in engineering practice, understanding their capabilities and limitations will be essential for career development.
Join the Conversation
Digital twins represent a convergence of disciplines, technologies and ideas. They challenge us to rethink how we represent reality, how we make decisions and how we design systems for the future.
The Digital Twin Dynamics: A Multi-Disciplinary Debate webinar on 24 June 2026, from 12:00 to 13:30, provides a timely opportunity to explore these questions with experts from across the IET community. It is free to attend and open to anyone interested in the evolving landscape of engineering and digital innovation.
Whether you are already working with digital twins or just beginning to explore their potential, this event promises to deliver valuable insights and spark meaningful discussion.
Register your place and be part of the conversation shaping the next generation of engineering practice. Register here!