Master’s in Simulation, Co-Simulation and HPC Engineering
Online master's programme of 1,900 hours in simulation, co-simulation and HPC engineering. 12 modules covering numerical methods, physical system modelling, FMI-based co-simulation, multiphysics, HPC architectures, parallel programming, performance and scalability, simulation with machine learning and digital twins under FMI 3.0, SSP 1.0 and ASME V&V 10-2019, with an assigned technical tutor.
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
Model physical systems with acausal and causal languages.
Apply stable numerical methods to differential systems.
Couple heterogeneous models using the FMI standard.
Solve multiphysics problems with partitioned schemes.
Programme parallel simulations with MPI, OpenMP and GPUs.
Analyse the performance and scalability of codes.
Integrate simulation, data and AI into digital twins.
Who it's for
- Simulation engineers who need to scale their models to HPC.
- HPC engineers supporting engineering simulation workloads.
- Co-simulation specialists integrating models from different tools.
- Multiphysics analysts working on coupled problems.
- Digital twin architects connecting simulation with real-time data.
- Scientific software developers writing parallel solvers.
- Engineering or science graduates looking to enter the automotive sector.
What you'll work with
- Modelica Association FMI 3.0 (Functional Mock-up Interface for model exchange and co-simulation)
- Modelica Association SSP 1.0 (System Structure and Parameterisation)
- ASME V&V 10-2019 (V&V in computational solid mechanics)
- ASME V&V 20-2009 (V&V in computational fluid dynamics and heat transfer)
- ISO/IEC 9899 (C programming language)
- ISO/IEC 1539-1 (Fortran programming language)
- ISO 23247-1 (digital twin framework for manufacturing)
- OpenModelica
- preCICE
- OpenFOAM
- Slurm
- ParaView
12 modules · 1900 h
1.1Types of models: continuous, discrete and hybrid1.2Ordinary differential and algebraic equations1.3Partial differential equations1.4Simulation in the V-model1.5Model-based engineering and MBSE1.6Fidelity and purpose of the model1.7From the physical model to the digital twin1.8Simulation tool ecosystem1.9Computational cost and scales1.10Case study: simulation strategy for a mechatronic product
2.1Explicit and implicit time integration2.2Stiffness and numerical stability2.3Runge-Kutta and BDF methods2.4Finite difference schemes2.5Finite element method2.6Finite volume method2.7Solving sparse linear systems2.8Iterative methods and preconditioners2.9Errors and convergence2.10Lab: integrating a stiff system with BDF
3.1Acausal modelling with Modelica3.2Libraries for mechanical, electrical and thermal systems3.3Modelling hydraulic systems3.4Energy-based modelling with bond graphs3.5Modelling control systems3.6Simulink and causal modelling3.7Model initialisation3.8Events and discontinuities3.9Validation of system models3.10Practical: Modelica model of an electromechanical system
4.1Principles and types of co-simulation4.2The FMI standard for model exchange4.3Model exchange versus co-simulation4.4Jacobi and Gauss-Seidel master algorithms4.5Communication steps and stability4.6Algebraic loops between subsystems4.7The SSP standard for systems of systems4.8Distributed co-simulation over a network4.9Orchestration tools4.10Lab: co-simulation of two FMUs with a master algorithm
5.1Fluid-structure coupling5.2Thermomechanical coupling and thermal expansion5.3Electromagnetism and heat5.4Strong and weak coupling5.5Interpolation between meshes5.6The preCICE coupling library5.7Stability of partitioned schemes5.8Validation of multiphysics cases5.9Cost and parallelisation5.10Workshop: fluid-structure coupling with preCICE
6.1Multi-core processors and memory hierarchy6.2Clusters and interconnection networks6.3GPUs and accelerators6.4Parallel file systems6.5Supercomputers and the EuroHPC network6.6Metrics: FLOPS, bandwidth and the roofline model6.7Slurm job schedulers6.8Containers in HPC6.9Cloud computing for HPC6.10Practical: roofline analysis of a compute kernel
7.1Data and task parallelism7.2Shared memory with OpenMP7.3Distributed memory with MPI7.4Domain decomposition7.5Collective communications in MPI7.6GPU programming with CUDA7.7Portability with Kokkos and SYCL7.8Hybrid MPI and OpenMP parallelism7.9Debugging parallel code7.10Lab: Poisson solver with MPI and OpenMP
8.1Amdahl's and Gustafson's laws8.2Strong and weak scaling8.3Profiling with performance tools8.4Load balancing8.5Memory optimisation and vectorisation8.6Parallel input and output with HDF58.7Energy efficiency in HPC8.8Checkpointing and fault tolerance8.9Application benchmarks8.10Workshop: scalability study of a CFD code
9.1Surrogate models of simulations9.2Physics-informed neural networks9.3Neural operators for partial differential equations9.4Reduced-order models9.5Learning from simulation results9.6HPC workflows with AI9.7Accelerated design optimisation9.8Generating training data9.9Validating surrogates9.10Practical: reduced-order model of a thermal field
10.1Scientific workflow managers10.2Automated parametric sweeps10.3Simulation data management10.4Post-processing and visualisation with ParaView10.5In situ visualisation10.6Reproducibility and traceability10.7Simulation process and data management (SPDM)10.8FAIR principles for simulation data10.9Cloud automation10.10Lab: automated parametric sweep on a cluster
11.1Verification and validation of simulations11.2Uncertainty in coupled models11.3Real-time digital twins11.4Real-time simulation and HIL11.5Model reduction for real time11.6Integration with sensor data11.7Industrial digital twin cases11.8Model governance11.9Quality standards in simulation11.10Case study: digital twin of a thermal system
12.1Defining the system to be simulated12.2Selecting models and tools12.3Implementing the models12.4Coupling and co-simulation12.5Parallelisation and performance12.6Verification and validation12.7Analysis of results12.8Conclusions and future work12.9Master's Thesis report12.10Master's Thesis: defence before a panel
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 the intake, so you can combine the programme with your job.
Each module combines theory, guided reading of the original standard and applied exercises. The working software is OpenModelica, preCICE, OpenFOAM, Slurm and ParaView, in free, academic or evaluation versions. Real procedures are studied: co-simulation of mechatronic systems with FMI, scalability studies on an HPC cluster, and fluid-structure coupling. Assessment is continuous by module, plus the final Master's Thesis.
Standards covered: Modelica Association FMI 3.0, Modelica Association SSP 1.0, ASME V&V 10-2019, ASME V&V 20-2009, ISO/IEC 9899, ISO/IEC 1539-1 and ISO 23247-1.
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
- University degree in engineering, science or architecture, or equivalent demonstrable professional experience.
- 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 1,900 hours over 12–14 months. You set your own pace within each intake, and your tutor helps you plan the workload around your job.
You work with OpenModelica, preCICE, OpenFOAM, Slurm and ParaView, 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 Master's Thesis. 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 in a course or postgraduate diploma in the same area are recognised in the higher programme. Ask about the recognition policy when you enrol.
A university degree in engineering, science or architecture (or equivalent professional experience), a computer able to run the programme 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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