The Deep Ritz method: A deep learning-based numerical algorithm for solving variational problems

09/30/2017
by   Weinan E, et al.
1

We propose a deep learning based method, the Deep Ritz Method, for numerically solving variational problems, particularly the ones that arise from partial differential equations. The Deep Ritz method is naturally nonlinear, naturally adaptive and has the potential to work in rather high dimensions. The framework is quite simple and fits well with the stochastic gradient descent method used in deep learning. We illustrate the method on several problems including some eigenvalue problems.

READ FULL TEXT

page 1

page 2

page 3

page 4

research
09/28/2022

Deep learning for gradient flows using the Brezis-Ekeland principle

We propose a deep learning method for the numerical solution of partial ...
research
06/02/2022

Stochastic Deep-Ritz for Parametric Uncertainty Quantification

Scientific machine learning has become an increasingly popular tool for ...
research
06/10/2020

Stochastic Gradient Descent for Semilinear Elliptic Equations with Uncertainties

Randomness is ubiquitous in modern engineering. The uncertainty is often...
research
05/31/2021

Deep-Learning Discovers Macroscopic Governing Equations for Viscous Gravity Currents from Microscopic Simulation Data

Although deep-learning has been successfully applied in a variety of sci...
research
06/21/2022

Robust SDE-Based Variational Formulations for Solving Linear PDEs via Deep Learning

The combination of Monte Carlo methods and deep learning has recently le...
research
07/07/2020

Structure Probing Neural Network Deflation

Deep learning is a powerful tool for solving nonlinear differential equa...

Please sign up or login with your details

Forgot password? Click here to reset