How Do Physics-Informed Neural Networks Work?

The video tutorial introduces physics informed neural networks, explaining their significance in machine learning. It covers the basics of neural networks, optimization, and the critical role of loss functions. The tutorial highlights how physics equations can inform loss functions, improving model accuracy. It includes examples like Burgers and Schrodinger's equations, demonstrating practical applications and code insights.

What challenges are associated with solving differential equations in modeling systems?

Describe the significance of the Burgers equation in fluid dynamics as mentioned in the video.

How does the mean squared error function relate to the physics informed neural network?