Gaussian process regression + deep neural network autoencoder for probabilistic surrogate modeling in nonlinear mechanics of solids

Documenti analoghi

• MAgNET: A Graph U-Net Architecture for Mesh-Based Simulations
  di: Deshpande, Saurabh, et al.
  Pubblicazione: (2022)
• Quantum-informed simulations for mechanics of materials: DFTB+MBD framework
  di: Shen, Zhaoxiang, et al.
  Pubblicazione: (2024)
• Domain decomposition of large neural network surrogate models
  di: Gödde, Timm, et al.
  Pubblicazione: (2026)
• A clustering adaptive Gaussian process regression method: response patterns based real-time prediction for nonlinear solid mechanics problems
  di: Li, Ming-Jian, et al.
  Pubblicazione: (2024)
• PDE-constrained Gaussian process surrogate modeling with uncertain data locations
  di: Ye, Dongwei, et al.
  Pubblicazione: (2023)