Physics-informed Machine Learning: A Comprehensive Overview

🧩Physics-informed machine learning uses optimization and machine learning techniques to incorporate and discover physics in complex systems.

🌌Parsimonious modeling involves learning differential equations from measurement data using machine learning algorithms.

🧠Physics-informed neural networks leverage known physics to improve the training and generalization of neural network models.

🧪Operator methods focus on learning solution operators of differential equations to model complex systems.

🌐Digital twins integrate physics-based models with real-world measurements to optimize and design complex engineering systems.

What is physics-informed machine learning?

—Physics-informed machine learning is a field that combines physics and machine learning techniques to incorporate and discover physics in complex systems.

What are parsimonious models?

—Parsimonious models involve learning differential equations from measurement data using machine learning algorithms.

How do physics-informed neural networks work?

—Physics-informed neural networks leverage known physics to improve the training and generalization of neural network models.

What are operator methods?

—Operator methods focus on learning solution operators of differential equations to model complex systems.

What are digital twins?

—Digital twins integrate physics-based models with real-world measurements to optimize and design complex engineering systems.

00:00Introduction to the course on physics-informed machine learning.

05:42Overview of parsimonious models for learning differential equations from data.

13:31Explanation of physics-informed neural networks that incorporate known physics in the training process.

22:30Discussion on operator methods and learning solution operators of differential equations.

31:18Exploration of symmetry and its role in machine learning algorithms.

39:14Introduction to digital twins and their integration of physics-based models with real-world measurements.

47:59Case studies and benchmarks that demonstrate the application of physics-informed machine learning.