Performance Prediction

Performance prediction aims to estimate the performance of systems or algorithms without exhaustive testing, saving significant computational resources and time. Current research focuses on developing accurate predictive models using diverse techniques, including graph neural networks, transformer-based models, and ensemble learning methods, often leveraging features extracted from various data representations (e.g., molecular structures, program code, architectural graphs). These advancements are crucial for optimizing resource allocation in diverse fields, from biopharmaceutical manufacturing and large language model development to algorithm selection and engineering design, enabling more efficient and effective decision-making.