Deep Operator Networks (DeepONet) - Physics Informed Machine Learning
Offered By: Steve Brunton via YouTube
Course Description
Overview
Explore the concept of Deep Operator Networks (DeepONet) in the context of Physics Informed Machine Learning through this 17-minute video lecture. Delve into the central idea behind DeepONets, understand the solution operator, and examine a practical example application with results. Learn about this innovative approach to machine learning in physics, produced at the University of Washington with funding support from the Boeing Company.
Syllabus
Intro
DeepONets: Central Idea
// The Strawman
What is the Solution Operator?
// How are DeepONets Trained?
DeepONet Example Application/Results
Outro
Taught by
Steve Brunton
Related Courses
Deep Learning to Discover Coordinates for Dynamics - Autoencoders & Physics Informed Machine LearningSteve Brunton via YouTube Machine Learning in Fluid Dynamics and Climate Physics
Alan Turing Institute via YouTube Uncertainty Quantification with Physics-Informed Machine Learning
Alan Turing Institute via YouTube Unique Challenges in Physics-Informed Machine Learning
Alan Turing Institute via YouTube Physics Informed Machine Learning: High-Level Overview of AI and ML in Science and Engineering
Steve Brunton via YouTube