Master thesis: Physics-informed machine learning for variable amplitude loading in fatigue crack growth
Marknesse, Flevoland, Nederland
EUR 32K-33K (estimate) Entry-level Full Time
Tasks
- Add uncertainty quantification
- Assess physics informed machine learning for variable amplitude fatigue crack modeling
- Benchmark model performance against state of the art methods
- Conduct literature review for physics informed machine learning in prognostics and health management
- Design and implement physics informed machine learning framework
- Find relationships between terms in physical fatigue crack growth models
- Model variable amplitude fatigue crack growth
- Predict variable amplitude fatigue crack growth
- Provide interpretability
- Support aircraft structural analysis
- Validate framework using real world datasets
Perks/Benefits
- Conference or journal publication opportunity
- Internship allowance
- Technical staff support
- Weekly supervision
Skills/Tech-stack
Crack Growth | Fatigue crack growth | Health management | Machine Learning | Physics-informed machine learning | Prognostics and Health Management | PyTorch | Python | Uncertainty Quantification
Education
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