Diploma in Partial Twins and Degradation Assessment
Online postgraduate diploma of 800 hours in partial twins and degradation assessment. 8 modules covering physical, reduced-order and hybrid degradation models, Bayesian data assimilation, remaining useful life prognosis and twin deployment under ISO 23247, ISO 13381-1 and FMI 3.0, with an assigned technical tutor.
Diploma in Partial Twins and Degradation Assessment — studying from the United States
Data science, artificial intelligence and simulation are central to American industry, with major concentrations in the San Francisco Bay Area, Seattle, New York and Boston. Engineering organizations increasingly rely on machine learning, digital twins and high-performance computing to accelerate design, testing and operations. Employers range from large technology companies to aerospace, automotive and energy firms that apply analytics to complex systems. Demand is strong for engineers who combine domain knowledge with modeling, statistics and software skills.
Fees for the United States: US$1,670 (US Dollars), with instalments and employer sponsorship available.
Relevant bodies in the United States
Typical roles
Questions from the United States
Which US bodies relate to data science, AI and simulation engineering?
There is no single licensing body for data science or AI in the US. Relevant professional organizations include the IEEE Computer Society, the Association for Computing Machinery (ACM), and the Society for Industrial and Applied Mathematics (SIAM). Federal guidance such as the NIST AI Risk Management Framework shapes practice. SEIUM's qualifications are private and not accredited by a national body, so recognition is decided by employers.
How do US employers in data and AI regard private online postgraduate qualifications?
US tech firms, financial institutions and research labs generally hire on the basis of portfolios, technical interviews, relevant experience and sometimes advanced degrees. A private online qualification can show structured study, but it is not a standard hiring requirement and each employer weighs it differently. Public projects, code repositories and measurable results tend to matter most.
Is it realistic to study data and AI from the US around a full-time job?
Yes, the programmes are fully online and taught in English, so you can study from home without a student visa. The workload is large: about 1,900 hours for a master's, 800 hours for a postgraduate diploma, and 300 hours for a specialist course. An assigned technical tutor supports you. Fees may be paid in instalments, and employer sponsorship is sometimes available, which you should confirm with your employer.
Take the controls.
Run a real engineering trade-off from this field. Move the sliders, watch the model react in real time and beat the challenge.
What you will be able to do
Define the appropriate scope of a partial digital twin.
Model degradation mechanisms with physical laws.
Build reduced-order models for fast execution.
Combine physics and data in hybrid models.
Update the twin through Bayesian data assimilation.
Estimate remaining life with uncertainty propagation.
Deploy interoperable, verified partial twins.
Who it's for
- Digital twin engineers who want to add degradation modelling and prognosis to their twins.
- Structural integrity engineers looking to move from periodic assessment to continuously updated models.
- Prognostics and health management (PHM) engineers.
- Simulation engineers who need fast reduced-order and hybrid models.
- Industrial data scientists working with physics-based models.
- Reliability engineers.
- Engineering or science graduates seeking to enter the energy sector.
What you'll work with
- ISO 23247 (Digital twin framework for manufacturing)
- ISO 13381-1 (Prognostics: general guidelines)
- Modelica Association FMI 3.0 (Functional Mock-up Interface)
- ASME V&V 10 (Verification and validation in computational solid mechanics)
- API 579-1/ASME FFS-1 (Fitness-for-service)
- IEC 63278-1 (Asset Administration Shell)
- ASTM E1049 (Fatigue cycle counting)
- MATLAB and Simulink
- Ansys Twin Builder
- Python with PyTorch and FilterPy
- OpenModelica
- PyMC
8 modules
1.1Full twin versus partial twin1.2Scope by component and by phenomenon1.3Degradation mechanisms: wear, fatigue and corrosion1.4Creep and thermal ageing1.5Degradation of batteries and electronics1.6Observable and hidden variables1.7Fidelity required by the decision1.8Architecture of a degradation twin1.9Use cases in industrial assets1.10Workshop: selecting the scope of a partial twin
2.1Paris law for crack growth2.2Miner's rule and cumulative damage2.3Rainflow cycle counting2.4Archard wear models2.5Arrhenius model for ageing2.6Electrochemical battery models2.7Reduced-order thermal models2.8Physical parameters and their identification2.9Limitations of physical models2.10Practical exercise: fatigue damage with rainflow and Miner
3.1The need for fast models3.2Proper orthogonal decomposition (POD)3.3Surrogate models with Gaussian processes3.4Neural networks as surrogates3.5Reduced finite element models3.6Design of experiments sampling3.7Validation of the reduced model3.8Real-time execution3.9Model reduction tools3.10Lab: Gaussian process surrogate
4.1Correcting physical models with data4.2Physics-informed neural networks (PINN)4.3Grey-box models4.4Residual learning4.5Physical constraints in training4.6Extrapolation beyond the data4.7Comparison with purely data-driven models4.8Interpretability of hybrid models4.9Selecting the approach according to the data4.10Practical exercise: hybrid tool-wear model
5.1Bayesian parameter estimation5.2Extended and unscented Kalman filters5.3Particle filters5.4Updating with inspection data5.5Model calibration with MCMC5.6Observability of the degradation state5.7Virtual state sensors5.8Twin update frequency5.9Detection of regime changes5.10Lab: particle filter for crack growth
6.1Defining the failure threshold6.2Remaining useful life prognosis6.3Uncertainty propagation6.4Prognostic metrics: alpha-lambda and PH6.5Future operating scenarios6.6Operating decisions to extend life6.7Risk-based inspection6.8Maintenance planning6.9Communicating probabilistic results6.10Case study: remaining life of an industrial battery
7.1Software architecture of the twin7.2Integration with plant data7.3Interoperability with FMI and FMU7.4Asset Administration Shell7.5Execution at the edge or in the cloud7.6Validation, verification and credibility7.7Model version management7.8Costs and return7.9Scaling to asset fleets7.10Workshop: architecture of a degradation twin
8.1Asset and degradation mechanism8.2Available operating and inspection data8.3Baseline physical model8.4Reduced or hybrid model8.5Data assimilation8.6Remaining life prognosis8.7Validation with field data8.8Deployment proposal for the degradation twin8.9Report and presentation8.10Final project: final review with the tutor
How you'll learn
100% online training on the SEIUM campus, with downloadable material, recorded classes and an assigned technical tutor. There are no fixed timetables: you progress at your own pace within each intake, so the programme is compatible with work.
Each module combines theory, guided reading of the original standard and applied exercises. The working software is MATLAB and Simulink, Ansys Twin Builder, Python with PyTorch and FilterPy, OpenModelica and PyMC, in free, academic or evaluation versions. Practical work is based on real procedures for Bayesian calibration of degradation models, remaining life prognosis with uncertainty and validation of reduced models against full simulation. Assessment is continuous by module plus the final project.
Standards covered: ISO 23247, ISO 13381-1, Modelica Association FMI 3.0, ASME V&V 10, API 579-1/ASME FFS-1, IEC 63278-1, ASTM E1049.
Build it end to end
Your final project follows the real engineering workflow, step by step, reviewed by your tutor.
Where this takes you
Requirements
- Prior technical training or professional experience in the sector. No university degree is required.
- Internet connection and a computer capable of running the programme software.
- English sufficient to read technical standards.
Ready to start?
Send your application today. Our admissions team replies within 72 hours with your study plan and funding options.
Frequently asked questions
The programme totals 800 hours over 6–8 months. You set your own pace within each intake, and your tutor helps you plan the workload around your job.
You work with MATLAB and Simulink, Ansys Twin Builder, Python with PyTorch and FilterPy, OpenModelica and PyMC, using free, academic or evaluation versions. Your tutor indicates the free alternative wherever one exists.
A SEIUM Institute diploma, awarded on passing the continuous assessment and the final project. It is SEIUM's own qualification, not an official state degree; its recognition depends on the programme completed and the regulations of each country.
Yes. Modules passed are recognised in a higher programme in the same area (postgraduate diploma or master's). Ask about the recognition policy when you enrol.
Prior technical training or professional experience in the sector (no university degree required), a computer able to run the software and English sufficient to read technical standards.
Start dates are set by SEIUM's faculty for each intake. Our admissions team will confirm the next available date when you apply.
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