Research map / Graph learning / GNN architectures
Rapid Training of Hamiltonian Graph Networks Using Random Features
Read the paperCheck your idea against 108,967 papers
AI-extracted summary. Written by AI from the paper's title only (no summary yet). Numbers and claims can be wrong or missing context, so check the paper before you cite or build on it.
Closest papers by meaning
- Unravelling the Performance of Physics-informed Graph Neural Networks for Dynamical Systems (NeurIPS 2022 · similarity 0.91)
- Learning Hamiltonian Dynamics at Scale: A Differential-Geometric Approach (ICML 2026 · similarity 0.90)
- Improving Long-Range Interactions in Graph Neural Simulators via Hamiltonian Dynamics (ICLR 2026 · similarity 0.89)
- Towards Cross Domain Generalization of Hamiltonian Representation via Meta Learning (ICLR 2024 · similarity 0.89)
- MaNGO — Adaptable Graph Network Simulators via Meta-Learning (NeurIPS 2025 · similarity 0.87)
- SEGNO: Generalizing Equivariant Graph Neural Networks with Physical Inductive Biases (ICLR 2024 · similarity 0.87)
Writing a paper in this area? scime draft is a free LaTeX editor with AI included, real-time co-authors and a check of your references and venue rules before you submit. It also imports OpenAI Prism projects.