Morphological evolution via surface diffusion learned by convolutional, recurrent neural networks: extrapolation and prediction uncertainty

Documenti analoghi

• Extreme time extrapolation capabilities and thermodynamic consistency of physics-inspired Neural Networks for the 3D microstructure evolution of materials via Cahn-Hilliard flow
  di: Lanzoni, Daniele, et al.
  Pubblicazione: (2024)
• Unified machine-learning framework for property prediction and time-evolution simulation of strained alloy microstructure
  di: Fantasia, Andrea, et al.
  Pubblicazione: (2025)
• Neural surrogates for crystal growth dynamics with variable supersaturation: explicit vs. implicit conditioning
  di: Rigoni, Matteo, et al.
  Pubblicazione: (2026)
• Phase-field Modelling of Anisotropic Solid-State Dewetting on Patterned Substrates
  di: Radice, Emma, et al.
  Pubblicazione: (2024)
• Quantitative analysis of the prediction performance of a Convolutional Neural Network evaluating the surface elastic energy of a strained film
  di: Encinar, Luis Martín, et al.
  Pubblicazione: (2024)