YoVDO

Physics Informed Machine Learning: Recap and Summary

Offered By: Steve Brunton via YouTube

Tags

Artificial Intelligence Courses Machine Learning Courses Control Theory Courses Dynamical Systems Courses Digital Twins Courses Physics Informed Neural Networks Courses Physics Informed Machine Learning Courses

Course Description

Overview

Save Big on Coursera Plus. 7,000+ courses at $160 off. Limited Time Only!
Recap and summarize the key concepts of Physics Informed Machine Learning in this 24-minute video lecture. Explore the five stages of machine learning and how physics can be integrated into each stage. Delve into various architectures, symmetries, digital twins, and applications in engineering. Gain insights into the importance of dynamical systems and controls benchmarks. Learn about future modules, curriculum frameworks, and the dual problems of PIML. Get a sneak peek into topics such as parsimonious models, PINNs, operator methods, and case studies. Enhance your understanding of the intersection between AI/ML and physics through this comprehensive overview.

Syllabus

Intro
Future Modules
Curriculum Framework
The Dual Problems of PIML
Data-Driven Science and Engineering
Sneak Peak of the Modules
Sneak Peak: Parsimonious Models
Sneak Peak: PINNs
Sneak Peak: Operator Methods
Sneak Peak: Symmetries
Sneak Peak: Digital Twins
Sneak Peak: Case Studies & Benchmarks
Outro


Taught by

Steve Brunton

Related Courses

Introduction to Artificial Intelligence
Stanford University via Udacity Probabilistic Graphical Models 1: Representation
Stanford University via Coursera Artificial Intelligence for Robotics
Stanford University via Udacity Computer Vision: The Fundamentals
University of California, Berkeley via Coursera Learning from Data (Introductory Machine Learning course)
California Institute of Technology via Independent