LTesan/MPNN-UnderReach
MPNN-UnderReach explores the under-reaching phenomenon in message-passing neural PDE solvers. We propose physics-guided lower bounds on the number of message passing iterations. These bounds link graph information propagation to the CFL condition and PDE type. Official code for Tesan L. & Iparraguirre M.M. (2025)
What's novel
MPNN-UnderReach explores the under-reaching phenomenon in message-passing neural PDE solvers. We propose physics-guided lower bounds on the number of message passing iterations. These bounds link graph information propagation to the CFL condition and PDE type. Official code for Tesan L. & Iparraguirre M.M. (2025)
Code Analysis
13 files read · 3 roundsImplements GNN-based PDE solvers (wave, Poisson, plastic collision) using PyTorch Geometric to empirically validate physics-guided lower bounds on message passing iterations required for accurate PDE prediction.
Strengths
The dual-edge-type MeshGraphNetContact architecture for contact mechanics is a genuine architectural contribution, and the rollout-based evaluation pipeline (one-step vs n-step) is well-structured for studying information propagation limits in GNNs.
Weaknesses
Severe code duplication between encoders.py and meshgraph_contact.py (nearly identical MLP/Encoder/Decoder classes), no unit tests at all, and the dataset classes repeat the same loading logic three times with minor variations.
Score Breakdown
Signal breakdown
Innovation
Craft
Traction
Scope
Evidence
Commits
18
Contributors
1
Files
690
Active weeks
2
Repository
Language
Python
Stars
2
Forks
0
License
GPL-3.0