Learning coupled Allen-Cahn and Cahn-Hilliard phase-field equations using Physics-informed neural operator(PINO)

Documenti analoghi

• Explicit adaptive time stepping for the Cahn-Hilliard equation by exponential Krylov subspace and Chebyshev polynomial methods
  di: Botchev, Mike A.
  Pubblicazione: (2025)
• Efficient and stable time integration of Cahn-Hilliard equations: explicit, implicit and explicit iterative schemes
  di: Botchev, M. A., et al.
  Pubblicazione: (2024)
• Surface profile recovery from electromagnetic field with physics--informed neural networks
  di: Chen, Yuxuan, et al.
  Pubblicazione: (2024)
• Unconditionally energy stable IEQ-FEMs for the Cahn-Hilliard equation and Allen-Cahn equation
  di: Chen, Yaoyao, et al.
  Pubblicazione: (2024)
• Sequential infinite-dimensional Bayesian optimal experimental design with derivative-informed latent attention neural operator
  di: Go, Jinwoo, et al.
  Pubblicazione: (2024)