Rate My Professor Hansani Weeratunge

Dr. Weeratunge's research focuses on integrating optimization and machine learning to tackle complex problems in energy systems and materials science. Her interests include renewable energy forecasting, underwater acoustic metamaterials, microgrid demand prediction, inverse design of metastructures, polymer coarse-graining, and heat pump systems. She employs techniques like deep neural networks, Bayesian optimization, graph neural networks, and convolutional neural networks. Key publications include "A machine learning enabled hybrid optimization framework for efficient coarse-graining of a model polymer" (2022), "A machine learning accelerated inverse design of underwater acoustic polyurethane coatings" (2022), "Distributed solar generation forecasting using attention-based deep neural networks for cloud movement prediction" (2026), "Interpretable SHAP-bounded Bayesian optimization for underwater acoustic metamaterial coating design" (2025), "Graph Neural Network Based Surrogate Model for Design Informed Structural Optimization" (2025), and her PhD-related papers such as "Model predictive control for a solar assisted ground source heat pump system" (2018, Energy). With over 290 citations, her work contributes significantly to sustainable energy and advanced materials.