Solving Partial Differential Equations using Adversarial Neural Networks

  1. Carlos Uriarte 1
  2. David Pardo 1
  3. Judit Muñoz-Matute 2
  4. Ignacio Muga 3
  1. 1 Universidad del País Vasco/Euskal Herriko Unibertsitatea
    info
    Universidad del País Vasco/Euskal Herriko Unibertsitatea

    Lejona, España

    ROR https://ror.org/000xsnr85

    Geographic location of the organization Universidad del País Vasco/Euskal Herriko Unibertsitatea
  2. 2 Basque Center for Applied Mathematics
    info
    Basque Center for Applied Mathematics

    Bilbao, España

    ROR https://ror.org/03b21sh32

    Geographic location of the organization Basque Center for Applied Mathematics
  3. 3 Pontificia Universidad Católica de Valparaíso
    info
    Pontificia Universidad Católica de Valparaíso

    Valparaíso, Chile

    ROR https://ror.org/02cafbr77

    Geographic location of the organization Pontificia Universidad Católica de Valparaíso
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Book:
Congress on Numerical Methods in Engineering CMN 2022 (2022. Las Palmas de Gran Canaria)
  1. David Greiner (ed. lit.)
  2. Irene Arias, (ed. lit.)
  3. Manuel Tur (ed. lit.)
  4. Gil Andrade-Campos (ed. lit.)
  5. Nuno Lopes (ed. lit.)
  6. J. Alexandre Pinho-da-Cruz (ed. lit.)

Publisher: International Center for Numerical Methods in Engineering (CIMNE)

ISBN: 978-84-123222-9-3

Year of publication: 2022

Pages: 289-289

Congress: Congress on Numerical Methods in Engineering (1. 2022. Las Palmas de Gran Canaria)

Type: Conference paper

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