Leveraging Hamilton-Jacobi PDEs with time-dependent Hamiltonians for continual scientific machine learning

Documenti analoghi

• HJ-sampler: A Bayesian sampler for inverse problems of a stochastic process by leveraging Hamilton-Jacobi PDEs and score-based generative models
  di: Meng, Tingwei, et al.
  Pubblicazione: (2024)
• Leveraging viscous Hamilton-Jacobi PDEs for uncertainty quantification in scientific machine learning
  di: Zou, Zongren, et al.
  Pubblicazione: (2024)
• Automatic discovery of optimal meta-solvers via multi-objective optimization
  di: Lee, Youngkyu, et al.
  Pubblicazione: (2024)
• A time-dependent symplectic network for non-convex path planning problems with linear and nonlinear dynamics
  di: Zhang, Zhen, et al.
  Pubblicazione: (2024)
• PI-SONet: A Physics-Informed Symplectic Operator Network for Real-Time Optimal Control of Multi-Agent Systems
  di: Varghese, Alan John, et al.
  Pubblicazione: (2026)