KARMAN SPACE PROGRAMME
ABOUT
The KSP AI-Lab is a student-led research lab focused on building high-fidelity digital twins and generative design tools for aerospace structures. We combine solid mechanics, simulation, experimental testing, and machine learning to design lighter, safer, and more optimised hardware—faster than traditional workflows allow.
DIGITAL TWINS AT THE CORE
OF OUR DESIGN PROCESS
Digital twins of structural components and assemblies are becoming essential in modern aerospace design. A digital twin is a rigorously validated, physics-accurate model of how hardware behaves under load.
By integrating analytical theory, high-fidelity simulation, and experimental data, our twins replace conservative guesswork with quantified, evidence-based margins. This lets engineers:
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Predict stress distributions, stiffness, buckling limits, fatigue behaviour, and failure modes with far greater precision than hand calculations or isolated FEA runs.
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Resolve design iterations virtually, instead of building multiple physical prototypes.
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Confidently remove unnecessary mass because safety factors are grounded in verified models, not broad approximations.
In aerospace, where structural mass drives performance, digital twins are the future: faster development, higher safety, and fundamentally more optimised designs.
TWO LABS
DIGITAL TWIN DEVELOPMENT
For students with strong foundations in solid mechanics and numerical methods, capable of:
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Building and validating structural and thermal digital twins
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Running and interpreting FEA and other simulation tools
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Fusing analytical theory, simulation, and test data into reliable models
GENERATIVE DESIGN & OPTIMISATION
Use of topology optimisation, diffusion-based generative models, and multi-objective evolutionary algorithms to automatically generate and refine high-performance aerospace components. Designs are constrained by manufacturability and validated through the lab’s digital-twin framework.
3 AREAS OF EXPERTISE
Machine Learning & Generative Algorithms
For students strong in applied maths, optimisation, and data-driven modelling. You’ll develop the learning layer that interprets residuals, refines parameters, and guides optimal testing.
Key Work:
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Train fast surrogates (MLPs, RBFs) to interpolate the atlas.
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Build active-learning / policy models to reduce uncertainty.
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Fuse analytical structure with ML to enable adaptive, self-improving parameter estimation.
Computational Engineering & Simulation
For students with foundations in solid mechanics and numerical simulation. You’ll build the physics core that defines how the twin predicts structural behaviour.
Key Work:
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Develop reduced-order models + FEA benchmarks.
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Generate high-resolution atlases mapping parameters response.
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Optimise solvers for rapid querying during real-time estimation.
Embedded Systems & Sensor Instrumentation
For students with strong electrical and electronic engineering skills, capable of:
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Designing and wiring sensor suites (strain gauges, pressure transducers, accelerometers, vibration sensors, load cells)
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Interfacing sensors with microcontrollers and DAQ systems
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Building actuator control circuits and robust hardware data pipelines feeding the lab’s digital twins and generative tools
