University of Cambridge > Talks.cam > Applied and Computational Analysis > R-adaptivity, deep learning and the deep ritz method

  Browse A-Z

Log in

Googledetails Microsoftdetails

🔐 Log In

Information on

Subscribing to talksdetails Finding a talkdetails Adding a talkdetails Disseminating talksdetails Help and Documentationdetails

ℹī¸ Information

R-adaptivity, deep learning and the deep ritz method

Download to your calendar using vCal

If you have a question about this talk, please contact Nicolas Boulle .

PINNS (physics informed neural nets) are becoming increasingly popular methods for using deep learning techniques to solve a wide variety of differential equations. They have been advertised as ‘mesh free methods’ which can out perform traditional methods. But how good are they in practice? In this talk I will look at how they compare with traditional techniques such as the finite element method on different types of PDE , linking their performance to that of general nonlinear approximation methods such as Free Knot Splines. I will show that a combination of ‘traditional’ numerical analysis and deep learning can yield good results. But there is still a lot to be learned about the performance and reliability of a PINN based method.

Joint work with Simone Appela, Teo Deveney, and Lisa Kreusser.

This talk is part of the Applied and Computational Analysis series.

This talk is included in these lists:

Note that ex-directory lists are not shown.

Other lists

Work or Play? Inside an entrepreneurial journey Cambridge Rape Crisis Public Lectures St John's College Old Divinity School

Other talks

Title tbc External Seminar - Michael Raissig TBC FILM: One With The Whale The Importance of Teaching about Science and Religion in a Time of Global Uncertainty Title to be confirmed Carrollian momenta from anti de Sitter
 

Š 2006-2025 Talks.cam, University of Cambridge. Contact Us | Help and Documentation | Privacy and Publicity