Notice: The reproducibility variables underlying each score are classified using an automated LLM-based pipeline, validated against a manually labeled dataset. LLM-based classification introduces uncertainty and potential bias; scores should be interpreted as estimates. Full accuracy metrics and methodology are described in Coakley et alK. L. Coakley, T. Snelleman, H. Hoos, and O. E. Gundersen, "The embrace of open science: An analysis of a decade of AI research and 56 800 conference papers," Under Review, 2026..
Grounding Graph Network Simulators using Physical Sensor Observations
Authors: Jonas Linkerhägner, Niklas Freymuth, Paul Maria Scheikl, Franziska Mathis-Ullrich, Gerhard Neumann
ICLR 2023 | Venue PDF | LLM Run Details
| Reproducibility Variable | Result | LLM Response |
|---|---|---|
| Research Type | Experimental | We experimentally validate our approach on a suite of prediction tasks for mesh-based interactions between soft and rigid bodies. Our method results in utilization of additional point cloud information to accurately predict stable simulations where existing Graph Network Simulators fail. |
| Researcher Affiliation | Academia | 1Institute for Anthropomatics and Robotics, Karlsruhe Institute of Technology, Karlsruhe, Germany 2Department Artificial Intelligence in Biomedical Engineering, Friedrich-Alexander-University Erlangen-Nürnberg, Erlangen, Germany |
| Pseudocode | No | The paper includes a detailed description of the Message Passing Network and Graph Network Simulator, along with a schematic diagram (Figure 7), but it does not present formal pseudocode or algorithm blocks. |
| Open Source Code | Yes | Code and data can be found under https://github.com/jlinki/GGNS. |
| Open Datasets | Yes | Code and data can be found under https://github.com/jlinki/GGNS. |
| Dataset Splits | Yes | We use a total of 675/135/135 trajectories for our training, validation and test sets. Each trajectory consist of T = 50 timesteps. |
| Hardware Specification | No | The paper mentions running experiments 'on a single GPU' (Table 4) but does not provide specific details such as the GPU model (e.g., NVIDIA A100, RTX 3090) or other hardware specifications like CPU or memory. |
| Software Dependencies | No | The paper mentions software like SOFA and Open3D but does not specify their version numbers, which is necessary for reproducible software dependencies. |
| Experiment Setup | Yes | We train all models on all tasks using the Adam optimizer (Kingma & Ba, 2015) with a learning rate of 5 10 4 and a batch size of 32, using early stopping on a held-out validation set to save the best model iteration for each setting. The models use a Leaky Re LU activation function, five message passing blocks with 1-layer MLPs and a latent dimension of 128 for node and edge updates. We use a mean aggregation for the edge features and a training noise of 0.01. |