A physics-informed deep learning framework for inversion and surrogate modeling in solid mechanics

  1. Haghighat, E.
  2. Raissi, M.
  3. Moure, A.
  4. Gomez, H.
  5. Juanes, R.
Journal:
Computer Methods in Applied Mechanics and Engineering

ISSN: 0045-7825

Year of publication: 2021

Volume: 379

Type: Article

DOI: 10.1016/J.CMA.2021.113741 GOOGLE SCHOLAR
Data source: Scopus