Discrete gradient flow approximations of high dimensional evolution partial differential equations via deep neural networks

Emmanuil H. Georgoulis*, Michail Loulakis, Asterios Tsiourvas

*Corresponding author for this work

Research output: Contribution to journalArticlepeer-review

3 Citations (Scopus)
23 Downloads (Pure)


We consider the approximation of initial/boundary value problems involving, possibly high-dimensional, dissipative evolution partial differential equations (PDEs) using a deep neural network framework. More specifically, we first propose discrete gradient flow approximations based on non-standard Dirichlet energies for problems involving essential boundary conditions posed on bounded spatial domains. The imposition of the boundary conditions is realized weakly via non-standard functionals; the latter classically arise in the construction of Galerkin-type numerical methods and are often referred to as “Nitsche-type” methods. Moreover, inspired by the seminal work of Jordan, Kinderlehrer, and Otto (JKO) Jordan et al. (1998), we consider the second class of discrete gradient flows for special classes of dissipative evolution PDE problems with non-essential boundary conditions. These JKO-type gradient flows are solved via deep neural network approximations. A key, distinct aspect of the proposed methods is that the discretization is constructed via a sequence of residual-type deep neural networks (DNN) corresponding to implicit time-stepping. As a result, a DNN represents the PDE problem solution at each time node. This approach offers several advantages in the training of each DNN. We present a series of numerical experiments which showcase the good performance of Dirichlet-type energy approximations for lower space dimensions and the excellent performance of the JKO-type energies for higher spatial dimensions.

Original languageEnglish
Article number106893
JournalCommunications in Nonlinear Science and Numerical Simulation
Early online date13 Oct 2022
Publication statusPublished - Feb 2023


  • Deep neural networks
  • Numerical analysis
  • Partial differential equations

ASJC Scopus subject areas

  • Numerical Analysis
  • Modelling and Simulation
  • Applied Mathematics


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