Summary
Artificial intelligence is increasingly used to guide scientific discovery, engineering design, and complex decision-making, where each experiment or trial can be costly and time-consuming. A central challenge is how to efficiently identify the most informative experiments from vast and complex design spaces, especially when observations are limited and uncertainty is high. This project develops a new paradigm for adaptive experimental design that enables learning systems to not only model data but also actively decide what data to acquire. The project's novelties are the integration of data r
