By the end of this course, participants will:
- Understand the foundational principles of both traditional finite element methods (FEM) and emerging scientific machine learning approaches for solving PDEs in computational mechanics.
- Gain fluency in modern AI-based solvers, including physics-informed neural networks (PINNs) and neural operators, with an appreciation of their mathematical structure, capabilities, and limitations.
- Draw meaningful parallels between FEM and neural network-based solvers to better understand where machine learning augments, complements, or diverges from classical approaches.
- Develop hands-on coding expertise in Matlab and Python/PyTorch through guided exercises that range from FEM implementation to training and evaluating ML-based surrogates.
- Learn to benchmark and compare solver performance across accuracy, generalizability, and computational efficiency using structured case studies.
- Acquire skills in prompt engineering for large language models to support rapid prototyping, code explanation, and documentation workflows.
Participants will leave the course with the theoretical understanding and practical experience needed to critically evaluate and implement solver strategies - both classical and machine-learned - for real-world problems in computational mechanics. ain a comprehensive understanding of the intersection of technology, art, and posthuman concepts. Whilst understanding the ability to critically evaluate the effects of AR might have on our present. After the course, and practically speaking, participants will have learnt how to build and produce artistic augmented works with TikTok Effect House, with hands-on workshops. They will have acknowledged how to guide their individual viewpoints and the theoretical influences, that their works can raise critical questions and opinions on where and how our post-human present is radically/ethically changing our technological understanding of our future and past.