Postgraduate Diploma in Continuous Modelling and Simulation
Online 800-hour postgraduate diploma in Continuous Modelling and Simulation. 8 modules with Modelica Language Specification 3.6, FMI 3.0 (Modelica Association) and MATLAB/Simulink. Assigned tutor and open enrolment.
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
Formulate state-space models of physical systems.
Build acausal multidomain models in Modelica and Simscape.
Select appropriate numerical integration methods.
Implement co-simulation using the FMI standard.
Identify model parameters from tests.
Quantify model uncertainty and credibility.
Apply simulation to controller design and verification.
Who it's for
- Modelling and simulation engineer looking to specialise in Continuous Modelling and Simulation.
- Control systems engineer looking to specialise in Continuous Modelling and Simulation.
- Digital twin analyst looking to specialise in Continuous Modelling and Simulation.
- Virtual validation engineer looking to specialise in Continuous Modelling and Simulation.
- R&D electronics engineer looking to specialise in Continuous Modelling and Simulation.
- Technical simulation consultant looking to specialise in Continuous Modelling and Simulation.
- Graduates in engineering or sciences seeking to enter the automotive sector.
What you'll work with
- Modelica Language Specification 3.6
- FMI 3.0 (Modelica Association)
- SSP 1.0 (Modelica Association)
- NASA-STD-7009A
- ASME V&V 10-2019
- IEEE 1516-2010 (HLA)
- ISO 11010-1:2022
8 modules · 800 h
1.1Ordinary differential equations in engineering1.2State variables and matrix representation1.3Linearisation about an equilibrium point1.4Lyapunov stability1.5Free and forced response1.6Transfer functions and Laplace1.7Stiff systems and time scales1.8Hybrid systems with discrete events1.9Parametric sensitivity analysis1.10Workshop: state model of a DC motor
2.1Electrical, mechanical and thermal analogies2.2Bond graphs2.3Acausal versus causal modelling2.4Non-linear circuits and components2.5Translational and rotational mechanics2.6Thermal systems and heat transfer2.7Hydraulic and pneumatic systems2.8Battery electrochemistry2.9Parameters from datasheets2.10Practice: multidomain model in Modelica
3.1Explicit and implicit Euler methods3.2Runge-Kutta and step-size control3.3Multistep BDF methods3.4Solvers for stiff systems3.5Differential-algebraic equations and index reduction3.6Event detection and localisation3.7Truncation error and numerical stability3.8Choice of solver and tolerances3.9Performance and simulation scaling3.10Laboratory: comparison of solvers in Python
4.1Simulink: blocks, subsystems and buses4.2Simscape for physical modelling4.3OpenModelica and standard libraries4.4SciPy and Julia DifferentialEquations4.5Circuit simulation with SPICE4.6Parameter and scenario management4.7Automation through scripting4.8Version control of models4.9Generation of simulation reports4.10Practice: DC-DC converter in Simscape
5.1Functional Mock-up Interface 2.0 and 3.05.2Model exchange versus co-simulation5.3FMU export and import5.4Co-simulation masters and communication step5.5System Structure and Parameterization (SSP)5.6Coupling with network simulators5.7Real-time simulation and determinism5.8Integration with HIL environments5.9Stability problems in co-simulation5.10Laboratory: co-simulation of two FMUs
6.1System identification from experimental data6.2Parameter estimation by least squares6.3Grey-box and black-box models6.4Design of excitation experiments6.5Fit metrics and cross-validation6.6Uncertainty quantification6.7Monte Carlo analysis6.8Model credibility according to NASA-STD-70096.9Traceability between requirements and models6.10Case study: identification of a thermal plant
7.1PID controller design on the model7.2State-space control7.3Model-in-the-loop and software-in-the-loop7.4Simulation-based design optimisation7.5Reduced-order models7.6Digital twins in operation7.7Fault and degradation simulation7.8Automatic code generation7.9Requirements verification by simulation7.10Practice: speed control with MIL and SIL
8.1System selection and requirements8.2Physical modelling of the system8.3Choice of solver and configuration8.4Identification of real parameters8.5Validation against measured data8.6Export as FMU and co-simulation8.7Sensitivity and uncertainty study8.8Technical report of results8.9Written report and presentation8.10Final review with the tutor
How you'll learn
Training 100 % online on the SEIUM campus, with downloadable material, recorded classes and assigned technical tutor. No fixed timetable: students progress at their own pace within the intake, which allows them to combine it with work.
Each module combines theory, guided reading of the original standard and applied exercises. The working software is MATLAB/Simulink, Simscape, OpenModelica, Python with SciPy, LTspice, in free, academic or evaluation versions. Tests are studied using real procedures for validating models against experimental data, component co-simulation using FMUs, sensitivity studies and Monte Carlo. Assessment is continuous per module plus the final integrating project.
Build it end to end
Your final project follows the real engineering workflow, step by step, reviewed by your tutor.
Formulate state-space models of physical systems.
Build acausal multidomain models in Modelica and Simscape.
Select appropriate numerical integration methods.
Implement co-simulation using the FMI standard.
Identify model parameters from tests.
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's software.
- Sufficient Spanish to follow the reading of 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 comprises 800 hours in total, spread over 6 to 8 months. That is between 10 and 15 hours a week on average, compatible with a full-time job.
The programme uses MATLAB/Simulink, Simscape, OpenModelica, Python with SciPy, LTspice. Free, academic or evaluation versions are used, and the tutor indicates the free alternative where one exists.
A SEIUM own diploma upon passing the continuous assessment and the integrating project. It is in-house training, not an official qualification from the Ministry.
Yes. Modules passed in a course or postgraduate diploma in the same area are recognised towards the advanced programme. Check the credit transfer policy when you enrol.
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