Riemann Tensor Neural Networks: Learning Conservative Systems with Physics-Constrained Networks

Documenti analoghi

• Dual Natural Gradient Descent for Scalable Training of Physics-Informed Neural Networks
  di: Jnini, Anas, et al.
  Pubblicazione: (2025)
• Physics-constrained DeepONet for Surrogate CFD models: a curved backward-facing step case
  di: Jnini, Anas, et al.
  Pubblicazione: (2025)
• Gauss-Newton Natural Gradient Descent for Physics-Informed Computational Fluid Dynamics
  di: Jnini, Anas, et al.
  Pubblicazione: (2024)
• cuVegas: Accelerate Multidimensional Monte Carlo Integration through a Parallelized CUDA-based Implementation of the VEGAS Enhanced Algorithm
  di: Tolotti, Emiliano, et al.
  Pubblicazione: (2024)
• Curvature-Aware Optimization for High-Accuracy Physics-Informed Neural Networks
  di: Jnini, Anas, et al.
  Pubblicazione: (2026)