Digital Twins: Enhancing Model-Based Design with AR, VR and MR (online)


Digital Twins: Enhancing Model-based Design with Augmented Reality, Virtual Reality, and Mixed Reality 


The idea of "digital twins" originated with NASA. Digital twins were then adopted into the manufacturing industry as a conceptual version of the Product Lifecycle Management (PLM).

Engineering systems have always used abstraction techniques to model complex problems. But the digital twin takes this idea further by allowing you to model and simulate a problem.

Thus, the digital twin is a virtual model that incorporates all the necessary information about a physical ecosystem to solve a particular problem - typically involving a simulation process. 

Other industries have adopted the digital twins paradigm with complex processes, such as construction (built environments) and healthcare. This course covers digital twins' overall design and development principles but emphasises using digital twins for manufacturing and construction (built environments). The digital twin is also a key component of the metaverse.

In this course, you will learn the following:

  • The use of Machine learning and Deep Learning techniques (collectively referred to as artificial intelligence (AI)) in developing and deploying digital twins
  • How to use simulation techniques with digital twins. 
  • Modelling digital twins using augmented reality (AR), virtual reality (VR), and other strategies for complex problems.  
  • Learning and pedagogy in AR/VR systems for education
  • Responsible AI for digital twins
  • AR/VR and digital twins
  • Simulation techniques for digital twins: agent-based modelling, systems dynamics, discrete event simulation
  • Applying digital twins to 'model-based design.' Model-based design help engineers and scientists design and implement complex dynamic systems using virtual (digital) modelling technologies. As a result, you can iterate your design through fast, repeatable tests. In addition, you can automate the end-to-end lifecycle of your project by connecting virtual replicas of physical components in a digital space. Once the system is modelled as a twin, various existing and new engineering problems, such as predictive maintenance and anomaly detection, can be modeled and simulated. 
  • Implementing digital twins in Python, MATLAB (MathWorks), and Unity – but prior knowledge of these systems is not needed. 
  • Application of digital twins to manufacturing and construction problems
  • Using the digital twin to design and implement use cases and services in the metaverse

The course takes a case study approach in the form of motivating case studies where we apply digital twin perspectives to real-life problems. The course involves code walkthroughs but not hands-on coding.


The course targets aspiring and seasoned simulation engineers and industry practitioners who want to develop digital twin models. Prior knowledge of engineering or built environments in any discipline is preferred, but prior knowledge of coding is optional. 

Note: for the first time this year, the course covers digital twins for built environments (construction). Details for this section will be updated soon. But if you have any questions, please contact us. 

Programme details

Dates, Times and Delivery

This course will run over six live online sessions on Mondays, Wednesdays, and Fridays, from 14:00 - 18:30, with a half-hour break in-between. 

Session dates:

  • Monday 9 October
  • Wednesday 11 October
  • Friday 13 October
  • Monday 16 October
  • Wednesday 18 October and
  • Friday 20 October. 

Sessions will be 14:00 to 18:30 (UK time). In some cases, the sessions will extend to 19:00, and will be delivered online via Microsoft Teams.

A world clock, and time zone converter can be found here:

No attendance at Oxford is required and you do not need to purchase any software.


Participants who attend the full course will receive a University of Oxford electronic certificate of attendance. 

The certificate will show your name, the course title and the dates of the course you attended.

You will be required to attend all of the live sessions on the course in order to be considered for an attendance certificate. 


Description Costs
Course fee £1095.00


All courses are VAT exempt.

Register immediately online 

Click the “book now” button on this webpage. Payment by credit or debit card is required.

Request an invoice

If you require an invoice for your company or personal records, please complete an online application form. The Course Administrator will then email you an invoice. Payment is accepted online, by credit/debit card, or by bank transfer. Please do not send card or bank details via email.


The tutors listed below may be subject to change.

Ajit Jaokar

Course Director

Based in London, Ajit's work work spans research, entrepreneurship and academia relating to artificial intelligence (AI) and the internet of things (IoT). 

He is the course director of the course: Artificial Intelligence: Cloud and Edge Implementations. Besides this, he also conducts the University of Oxford courses: Developing AI Applications and Computer Vision.

Ajit works as a data scientist through his company feynlabs - focusing on building innovative early stage AI prototypes for domains such as cybersecurity, robotics and healthcare.

Besides the University of Oxford, Ajit has also conducted AI courses in the London School of Economics (LSE), Universidad Politécnica de Madrid (UPM) and as part of the The Future Society at the Harvard Kennedy School of Government.

He is also currently working on a book to teach AI using mathematical foundations at high school level. 

Ajit was listed in the top 30 influencers for IoT for 2017 along with Amazon, Bosch, Cisco, Forrester and Gartner by the German insurance company Munich Re.

Ajit publishes extensively on KDnuggets and Data Science Central.

He was recently included in top 16 influencers (Data Science Central), Top 100 blogs (KDnuggets), Top 50 (IoT central), and 19th among the top 50 twitter IoT influencers (IoT Institute). 

His PhD research is based on AI and Affective Computing (how AI interprets emotion).

Ms Ayşe Mutlu


Ayşe Mutlu is a data scientist working on Azure AI and devops technologies. Based in London, Ayşe’s work involves building and deploying Machine Learning and Deep Learning models using the Microsoft Azure framework (Azure DevOps and Azure Pipelines).

She enjoys coding in Python and contributing to Open Source Initiatives in Python.

Rajkumar Bondugula


Rajkumar Bondugula has earned a M.S. and a Ph.D. from University of Missouri-Columbia, USA, both in Computer Science with a specialization in Artificial Intelligence (AI). He has co-authored 20 peer-reviewed scientific publications, a book titled "Application of Fuzzy Logic in Bioinformatics", 16 patent applications and his work has been cited over 250 times in the academic literature. He is a frequent guest lecturer in multiple universities, an executive faculty at Emory University Continuing Education, and a frequent podcast guest speaker. In nearly two decades, he has professionally used AI for computer vision, computational biology, e-commerce, social intelligence, Fintech and Telecom. In addition, he is also an expert in natural language processing and distributed computing. 

 He joins us from Verizon, where he is currently an AI Luminary Scientist and Data Science Fellow. He leads Digital Twin initiatives and is responsible for AI and Simulation modeling aspects of Digital Twins. In addition, he also leads the development of Cognitive Classifiers used for corporate data management.  

Before Verizon, Raj was a Distinguished Scientist and a Fellow at Equifax. He was responsible for leading a team of Data Scientists and Big Data Engineers to develop innovative solutions to hard problems that lead to organizational growth in 3-5 years.  Prior to Equifax, he did a brief stint at a startup called Shoutlet, established data science practice at Home Depot, lead a machine learning team at Sears Holdings Corporation and was a scientist at Department of Defense Biotechnology High Performance Computing Software Applications Institute. 

Dr Didem Gurdur Broo


Center for Design Research, Stanford University

Didem cares about the future of the world and nature. She is a computer scientist with a PhD in mechatronics, which can give you an idea about how much she loves to talk about the future and emerging technologies. She is a data person, always finds a way to talk about how important it is to know your data, use it to make decisions, and at some point, expect her to talk about art, visualizations, and visual analytics. Didem is a person who does not hesitate to talk about inequalities and point out her ethical concerns. She dreams of a better world and actively works on improving inequalities regardless of their nature. She is an analytical thinker with a passion for design thinking, a researcher with a future perspective, an engineer who likes problems more than solutions, and a teacher who likes to play during lectures. She is a good reader, sailor, divemaster, photographer, and drone pilot.

Currently, Didem is Marie Skłodowska-Curie Fellow on Human-centered and Sustainable Cyber-physical Systems at Stanford University. Her project focuses on intelligence, autonomy, and interoperability of cyber-physical systems. She uses data science, design thinking, future thinking, and systems thinking as guiding principles to design future intelligent and autonomous systems. The project is funded by European Commission's prestigious Marie Skłodowska-Curie Actions which supports excellence in research and innovation. Prior to this project, she was at the Engineering Department of the University of Cambridge as a research associate for several years. She has worked at the intersection of data science and engineering projects with a focus on the design and implementation of digital twins for cyber-physical systems. In addition to being an IEEE Senior Member, she also sits on the advisory board of several organisations and support their strategic development on topics related to responsible artificial intelligence/data science, sustainable technology and equal STEAM education activities for young women.

Dr Francesco Ciriello


Lecturer in Engineering Education, King’s College London

Francesco is a Lecturer in the Department of Engineering at King’s College London, where he teaches interdisciplinary design and mechatronics. He previously worked in the Education Group at MathWorks and provided consultancy services to educators and researchers on the use of MATLAB & Simulink. Francesco has broad expertise in Simulation and Artificial Intelligence, with application to Robotics & Control systems, signal processing and IoT. He also holds a PhD in Engineering from the University of Cambridge for his work in experimental fluid dynamics and a MEng in Civil Engineering from Imperial College London. 

Dirk Hartmann


Dirk Hartmann is a distinguished scientist, intrapreneur, and thought leader in the field of Simulation and Digital Twins.  

Among many distinctions, he has been awarded the prestigious Wernervon-Siemens Top Innovator by the Siemens CEO and CTO for generating novel products and services cross product lines through his innovations.  

In his career he took several leading roles in research, innovation, and development including a lead of a 2-digit million Siemens R&D program and the technical leadership for the Simulation & Digital Twin field at Siemens Technology.  

He is a passionate mentor, teacher, and supervisor for the next generation of innovators and experts exploring jointly promising Digital Twin solutions for the industry.  

Beyond this, he is a member of several high-level international conference committees and associations like EU-MATH driving industrial mathematics both on a national, European, and international level. 

Dr Sebastiaan J. van Zelst


Dr. ir. Sebastiaan J. van Zelst is a computer scientist with an entrepreneurial mindset.

After finishing his Ph.D. in 2019 (topic: online process mining), he worked at the Fraunhofer Institute for Applied Information Technology (FIT) as a post-doctoral researcher.
Since 2021 he has been leading FIT’s process mining research group, part of the department of Data Science and Artificial Intelligence, in which he holds the position of deputy department head.

Sebastiaan founded the open-source python-based process mining library pm4py (, the largest open-source process mining solution (over 750.000 downloads).
He has co-founded the Center for Process Intelligence (

He is the CEO and Co-Founder of PINC UG, which, in collaboration with Fraunhofer FIT, develops the PMTK process mining solution (
Sebastiaan has published several academic works in the field of process mining in both highly ranked journals and conferences. 

Dr Yogita  Khedkar-Chaudhar

Course Tutor Yogita Khedkar-Chaudhar has 11 years of teaching experience and 15 years of counselling experience in Clinical Psychology.
She is assistant professor at New Arts, Commerce & Science College, Ahmednagar and a practising clinical psychologist. 

She is interested in the future of education, including new pedagogies for inclusion.
Her work during COVID with students and the community has made her realize the need for new modes of engagement with learners to master skills rapidly and in non-conventional ways.

Yogita's research includes topics such as Emotional Maturity, game addiction, occupational stress, mental health and Women’s Mental Health, Statistical Analysis in Psychology, Social Adjustment and Motivational Conflicts.
Therapies she has practised include Cognitive Therapy, Behavioural Therapy, CBT, Person Centred Therapy, Psychoanalysis, Relaxation Techniques, Self-Hypnosis, Existential Therapy, Reality Therapy, Music Therapy.

She is working on pedagogies for learning in virtual environments. 

Tae Kim


Dr Lars Kunze

Course Director

Lars Kunze is a Departmental Lecturer in Robotics in the Oxford Robotics Institute (ORI) and the Department of Engineering Science at the University of Oxford. At ORI, he leads the Cognitive Robotics Group (CRG).

Lars is also a Stipendiary Lecturer in Computer Science at Christ Church, a Programme Fellow of the Assuring Autonomy International Programme (AAIP), and an Editor of the German Journal of Artificial Intelligence (KI Journal, Springer).

His areas of expertise lie in the fields of robotics and artificial intelligence (AI). His goal is to enable robots to understand their surroundings, to act autonomously, and to explain their own behaviour in meaningful human terms. To this end, his research concerns the design and development of fundamental AI techniques for autonomous robot systems. He focusses on the combination of knowledge representation, reasoning, machine learning, and robot perception; motivated by applications in complex, real-world environments.

Lars studied Cognitive Science (BSc, 2006) and Computer Science (MSc, 2008) at the University of Osnabrück, Germany, and partly at the University of Edinburgh, UK.

He received his PhD (Dr. rer. nat.) from the Technical University of Munich, Germany, in 2014. During his PhD, Lars worked on methods for naive physics and common sense reasoning in the context of everyday robot manipulation. He contributed to several national, European and international projects including RoboHow, RoboEarth, and the PR2 Beta program.

In May 2013, Lars was appointed as a Research Fellow in the Intelligent Robotics Lab at the School of Computer Science at Birmingham University.  Here he worked on qualitative spatio-temporal models for perception planning and knowledge-enabled perception, contributing to the European research projects STRANDS and ALOOF.

He was a visiting researcher in the JSK Lab at the University of Tokyo, Japan (Summer 2011) and the Human-Robot Interaction Laboratory at Tufts University, US (Spring 2015).

Namrita Mahindro


Namrita is a senior strategic executive with CXO level success in leading organisations transform their businesses leveraging digital and technology.
Currently, she is the Chief Digital Officer at Aditya Birla Chemicals, Filaments and Insulators (CFI)

Over the past two decades she has been both an entrepreneur and part of the corporate world across sectors (Agriculture, Automotive, Chemicals, Retail, Travel & Hospitality, Technology), working in India, US & UK with FTSE 100 companies like Inchcape, Tata Group (USD$100bn), Mahindra Group (USD$20billion) & Aditya Birla Group (ABG, USD$60bn)

An award winning digital transformation thought leader and practioner, Namrita’s forte lies in creating competitive advantage for organisations by re-defining business models, re-imagining customer experiences, re-engineering business processes, building people capabilities and orchestrating shifts in organisation culture. 

Over the past few years, particularly, Namrita has been leading the smart manufacturing & supply chain transformations working on building IIoT platforms which house digital twins for critical assets, driving energy optimisation, împroved asset & plant reliability, AR / VR  & cobots led safety & training initiatives; logistics platforms for better cost & productivity efficiency; third party integrated business planning platforms for better demand forecasting, supply planning & dynamic scenario planning for the VUCA world we live in.

A technology evangelist, Namrita is deeply committed to building data driven organisations; fostering a data & analytics culture; driving Responsible AI, defining data & AI ethics policy & governance; predicting & mitigating data privacy & security risks; enabling data based value creation & responsible innovation.

Namrita is also an Independent Board Director and a Shadow Board member at CFI.
Additionally, she is a Start Up Mentor and Life & Leadership Transformation Coach and gives back to community by teaching and serving as an advisory member across different industry bodies including NASSCOM’s Cloud Advisory committee, one of the founding member’s of  IAMAI’s Chief Digital Officer’s Club and across various committees at DMAi India

Namrita’s accolades include digital transformation awards for Taj Hotels, Club Mahindra, Mahindra Auto and Aditya Birla Chemicals.
More recently she was also the recipient of the CIO100 Game Changer Award by Foundry.
She was featured at TEDx Gateway Salon in Mumbai in November 2022 amongst the top ten women leaders at Aditya Birla Group who are Breaking Barriers and recognised in CXO Junction as one of the top 10 most followed Chief Digital Officer’s in India in Dec 2022.

Mr David Menard


With over 10 years of mixed reality and real-time development experience, David Menard is an industry-leader in virtual reality (VR) and augmented reality (AR) when it comes to enterprise applications.
At Unity Technologies, David oversees the technical developmentof Unity Reflect, which enables AEC (Architecture, Construction, Engineering) companies to create real-time experiences in augmented reality and virtual reality.
Prior to joining Unity, David led mixed-reality R&D efforts at the enterprise software giant, Autodesk.

Dr David McKee


Dr David Mc Kee, Chair of the Open Source, Standards, and Platform Stacks at the Digital Twin Consortium / CEO, CTO and founder, Slingshot Simulations

Dr David McKee is the CEO, CTO and founder at Slingshot Simulations, an enterprise fellow at the Royal Academy of Engineering, and chairs the technology working groups at the OMG Digital Twin Consortium. As CTO at Slingshot since 2019 David leads the company’s work on Digital Twins working across Cloud, IoT, and machine learning platforms.

At the Digital Twin Consortium David jointly lead the work on standardising a Digital Twin definition and continues to lead the Technology, Terminology, and Taxonomy working group. He is also responsible for leading the Open Source Initiative and a collaborative effort including Microsoft, Bentley Systems, DELL, and NTT to define a reference architecture for building Digital Twin Systems.

Before forming Slingshot David was a senior researcher at the University of Leeds building these systems for partners including the likes of Jaguar Land Rover and AliCloud.

Dr Tamara Monti



Tamara MONTI is the Education business leader for global software company Dassault Systèmes in Northern Europe, with the main objective to empower the workforce of the future.

She is working closely with Education and Industry leaders to demonstrate the value of the 3DExperience platform to upskill all engineers to speed up sustainable innovation.

She earned a PhD degree in Electromagnetics in 2013 and has been a visiting researcher at the Trieste Synchrotron, at Temple University of Philadelphia and at the University of Maryland at College Park working on microwave nanotechnology.
She held a postdoctoral position at the University of Nottingham from 2014 to 2017 on high power microwave material processing.

In 2017, she joined CST, subsequently acquired by Dassault Systèmes as part the SIMULIA brand, where she was one of the computational electromagnetic experts, supporting customers in the transportation and mobility industries.

Robbie Stevens

Course Tutor

Aero Technology Lead - Alpine F1 Team 

Robbie is the leader of the Aerodynamics Technology group at Alpine F1 Team. He joined the Team in 2016. His work includes the development of physics based aerodynamic models, diagnostic tools, data analysis and future methodology. In addition, he is also responsible for a number of technical and academic partnerships.  

Prior to joining the Team, Robbie was a Post-Doctoral Research Associate at the Cambridge University Engineering Department and a Clare College Cambridge Research Associate conducting research in high Mach number flows. 

Robbie received his Ph.D. from Cambridge University in 2015. His Ph.D. research involved the development of a reduced-order theoretical model to describe flapping-wing flight (at small bird/insect scales).

Robbie is also a chartered member of the Institute of Mechanical Engineers and the author of several published works in Aerodynamics and Fluid Mechanics.

Carina Mieth

Course Tutor

She recently joined the Operations Practice of McKinsey & Company. In this role, she is helping her clients make substantial and lasting improvements in their manufacturing performance.

She is a member of the core team of Women in AI & Robotics Germany, a network that promotes gender-inclusive, ethical and responsible AI solutions for the benefit of society.

In her last position at TRUMPF, she worked as a part-time "climate activist" using data and artificial intelligence to make machines and factories even more sustainable and energy efficient.

She worked as the advisor to the Managing Director R+D at TRUMPF Machine Tools. Her work focused on topics such as AI & robotics in industry, the implementation of the climate strategy in product development and the design of future sheet metal production systems.

For two years, she was the product owner in TRUMPF’s Smart Factory Simulation & Analytics team, which she built from scratch. She led the development of simulation model libraries & frameworks for the simple creation of digital twins of sheet metal productions.

She is a PhD candidate in mechanical engineering at the TU Dortmund University, where she is an associated member in the DFG-funded research training group adaption intelligence of factories in a dynamic and complex environment. In 2017, she completed her M.Sc. in electrical engineering and information technology with a specialization in control engineering from the Karlsruhe Institute of Technology.

Course aims

What you will learn 

  • Understand the main engineering applications in which digital twin technology is being used 
  • Explain the value of digital twin technology and model-based design in engineering practice 
  • Explain the difference between a simulation and a simulator 
  • Interpret the results of a MATLAB analysis 
  • Interpret the results of a Simulink simulation 
  • Explain different workflows to interface a digital twin to AR-VR-MR software, including playback, co-simulation, and integration.  
  • Implement a physical model for a component given a schematic representation 
  • Implement a data-driven model for a component given an experimental data set 
  • Playback the results of a simulation into AR-VR-MR software  
  • Run co-simulations between Simulink and AR-VR-MR software (UE4) 
  • Integrate a physical model in Simulink into AR-VR-MR software 
  • Model an engineering component using Simscape 
  • Optimise a design parameter in Simulink 
  • Critique the difference between white, grey, and black-box modelling approaches 
  • Specify a deployment pipeline for a digital twin in a production system 
  • Identify fidelity-performance trade-offs for simulations and real-time deployment  
  • Critique the role of emerging AR-VR-MR technologies in model-based engineering design 
  • Design an engineering model of a digital twin given a set of specifications and demonstrate integration within a 3D graphics simulator 
  • How to use AR and VR for modelling 
  • Fundamentals of augmented reality, virtual reality, and mixed reality  
  • Building professional AR/VR applications  
  • The AR/VR landscape for tools, technologies, and services


If you would like to discuss your application or any part of the application process before applying, please click Contact Us at the top of this page.

IT requirements

This course is delivered online using Microsoft Teams. You will be required to follow and implement the instructions we send you to fully access Microsoft Teams on the University of Oxford's secure IT network.

This course is delivered online; to participate you will need regular access to the Internet and a computer meeting our recommended Minimum computer specification.

It is advised to use headphones with working speakers and microphone.