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..
Predicting AC Optimal Power Flows: Combining Deep Learning and Lagrangian Dual Methods
Authors: Ferdinando Fioretto, Terrence W.K. Mak, Pascal Van Hentenryck630-637
AAAI 2020 | Venue PDF | LLM Run Details
| Reproducibility Variable | Result | LLM Response |
|---|---|---|
| Research Type | Experimental | The proposed model is evaluated on a large collection of realistic mediumsized power systems. The experimental results show that its predictions are highly accurate with average errors as low as 0.2%. |
| Researcher Affiliation | Academia | 1Georgia Institute of Technology, 2Syracuse University ffiorett@syr.edu, EMAIL, EMAIL |
| Pseudocode | Yes | Algorithm 1: Learning Step |
| Open Source Code | No | The paper does not provide a direct link to a code repository or explicitly state that the source code for the described methodology is being released. |
| Open Datasets | Yes | The experiments examine the proposed models on a variety of power networks from the NESTA library (Coffrin, Gordon, and Scott 2014). |
| Dataset Splits | Yes | The experiments use a 80{20 train-test split and report results on the test set. |
| Hardware Specification | No | No specific hardware details (like GPU/CPU models, memory) are mentioned for running the experiments. The paper only mentions 'GPU memory' in the conclusion, but not as part of the experimental setup. |
| Software Dependencies | No | The paper mentions using the Adam optimizer, but no specific software names with version numbers (e.g., PyTorch, TensorFlow, or specific solvers like CPLEX) are provided. |
| Experiment Setup | Yes | The training uses the Adam optimizer with learning rate (α 0.001) and β values p0.9, 0.999q and was performed for 80 epochs using batch sizes b 64. Finally, the Lagrangian step size ρ is set to 0.01. |