Graph Models
Graph Network-based Simulators
Introduced by Sanchez-Gonzalez et al. in Learning to Simulate Complex Physics with Graph NetworksGraph Network-Based Simulators is a type of graph neural network that represents the state of a physical system with particles, expressed as nodes in a graph, and computes dynamics via learned message-passing.
Source: Learning to Simulate Complex Physics with Graph Networks
Papers
| Paper | Code | Results | Date | Stars |
|---|
Tasks
| Task | Papers | Share |
|---|---|---|
| Benchmarking | 2 | 28.57% |
| Computational Efficiency | 1 | 14.29% |
| Fairness | 1 | 14.29% |
| Image Classification | 1 | 14.29% |
| Meta-Learning | 1 | 14.29% |
| Relational Reasoning | 1 | 14.29% |
Usage Over Time
Components
| Component | Type |
|
|---|---|---|
| 🤖 No Components Found | You can add them if they exist; e.g. Mask R-CNN uses RoIAlign |
