Self-supervised feature distillation and design of experiments for efficient training of micromechanical deep learning surrogates

Documenti analoghi

• Inverse designing surface curvatures by deep learning
  di: Guo, Yaqi, et al.
  Pubblicazione: (2023)
• Deep reinforcement learning for efficient exploration of combinatorial structural design spaces
  di: Hong, Chloe S. H., et al.
  Pubblicazione: (2025)
• Fourier-enhanced reduced-order surrogate modeling for uncertainty quantification in electric machine design
  di: Partovizadeh, Aylar, et al.
  Pubblicazione: (2024)
• Gaussian process regression + deep neural network autoencoder for probabilistic surrogate modeling in nonlinear mechanics of solids
  di: Deshpande, Saurabh, et al.
  Pubblicazione: (2024)
• Identifying heterogeneous micromechanical properties of biological tissues via physics-informed neural networks
  di: Wu, Wensi, et al.
  Pubblicazione: (2024)