Mechanical
Data-driven engineering, control, robotics and machine learning for physical systems.
Expert
Research-level depth and specialist material.
Dynamical Systems with Machine Learning
Modelling how systems change over time, then discovering those models from data with methods such as SINDy, DMD and Koopman analysis. For students comfortable with differential equations.
Steve Brunton (University of Washington)
Physics-Informed Machine Learning
How to build machine learning models that respect the laws of physics: adding conservation laws, symmetries and known equations to neural networks so they work with less data and generalise better. A strong bridge between classical mechanics and modern ML.
Steve Brunton (University of Washington)
NPTEL: AI and Machine Learning in Materials Engineering
IIT Kanpur course applying machine learning to materials data: property prediction, microstructure analysis and materials discovery.
NPTEL / IIT Kanpur