These lecture materials are openly available to everyone.
For students: You are encouraged to use these materials to support your studies.
For instructors: You are welcome to use, modify, and distribute these materials in your teaching.
No credit or reference to us is required.
This course develops a unified framework for integrating physics-based modeling, machine learning, control, and embedded deployment in engineering systems. The emphasis is on closing the loop from modeling to real-world execution under physical constraints. Physical AI is not only about learning models. It is about building systems that operate reliably in the real world.
Learning Objectives
Formulate learning problems with physical structure and constraints
Develop hybrid physics + data-driven models
Design control systems with learning components
Implement real-time AI on embedded hardware
Deploy models from simulation to real-world systems
Topics HTML Colab Slides PowerPoints PS Solution
[Part I: Foundations of Physical AI]
Introduction to Physical AI iColab
Dynamical Systems and Modeling iColab
Probabilistic Modeling and Estimation iColab
[Part II: Physics-Informed Learning]
Physics-Informed Neural Networks (PINNs) iColab
Neural Operators iColab
Constraints and Structure in Learning iColab
[Part III: Learning and Control]
Reinforcement Learning for Physical Systems iColab
Physics-Guided and Safe RL iColab
[Part IV: Sim-to-Real Transfer]
Sim-to-Real Gap and Domain Randomization iColab
System Identification and Residual Learning iColab
[Part V: Embedded Physical AI (Hands-on)]
Embedded Systems and Real-Time Control iColab
Edge AI and AI-in-the-Loop Control iColab
Sim-to-Real Deployment on Hardware iColab
System Integration & Demo iColab