IEEE

Synthetic Power Flow Data Generation Using Physics-Informed Denoising Diffusion Probabilistic Models

In smart grid, data-driven modules and applications rely on access to high-quality power flow data; however, real-world data are often limited due to privacy and operational constraints. This paper ...
IEEE

Model-Agnostic Empirical Evaluation of Test-Driven Prompt Engineering on Improving Accuracy and Efficiency in Large Language Models Python Code Generation

Abstract: Although Large Language Models (LLMs) are widely adopted for code generation, the generated code can be semantically incorrect, requiring iterations of evaluation and refinement. Test-driven ...