HyPINO: Multi-Physics Neural Operators via HyperPINNs and the Method of Manufactured Solutions

Documenti analoghi

• Pretrained Video Models as Differentiable Physics Simulators for Urban Wind Flows
  di: Perini, Janne, et al.
  Pubblicazione: (2026)
• Multi-Objective Loss Balancing for Physics-Informed Deep Learning
  di: Bischof, Rafael, et al.
  Pubblicazione: (2021)
• FC-PINO: High Precision Physics-Informed Neural Operators via Fourier Continuation
  di: Ganeshram, Adarsh, et al.
  Pubblicazione: (2022)
• Imposing Boundary Conditions on Neural Operators via Learned Function Extensions
  di: Mousavi, Sepehr, et al.
  Pubblicazione: (2026)
• HyResPINNs: A Hybrid Residual Physics-Informed Neural Network Architecture Designed to Balance Expressiveness and Trainability
  di: Cooley, Madison, et al.
  Pubblicazione: (2024)