New Course – “Machine Learning in Computational Mechanics”
A new course, Machine Learning in Computational Mechanics, will be introduced in the spring semester of the Joint Postgraduate Studies Program “Computational Mechanics” and will be included among the elective courses available for student enrollment.
The course is intended for students with an interest in computational mechanics, numerical simulation methods, data-driven modeling, as well as emerging trends in physics-informed machine learning and Artificial Intelligence (AI) in engineering. Its goal is to bridge modern machine learning with the needs of computational mechanics in engineering and simulation problems.
Lectures: Every Monday 16:00–18:45, at the PC Lab, School of Chemical Engineering (“I. Palyvos” Room).
Instructors:
- Michalis Kavousanakis, Assistant Professor
- Ioannis Kalogeris, Assistant Professor
Brief description:
The course “Machine Learning in Computational Mechanics” introduces modern machine learning and data-driven methods for engineering modeling and simulation. It covers supervised and unsupervised learning, neural networks, and physics-informed approaches for solving differential equations. Advanced topics include neural operators, generative AI, uncertainty quantification, and surrogate modeling in computational analysis.