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.