Model Based Optimization

Model-based optimization (MBO) aims to find the optimal input for a system by building and optimizing a surrogate model of its behavior, often when direct evaluation is expensive or impossible. Current research emphasizes robust methods for handling uncertainty and distribution shifts in offline settings, employing diverse techniques such as Gaussian processes, ensembles of randomized trees, diffusion models, and polynomial surrogates, often within a reinforcement learning framework. These advancements are crucial for tackling complex design problems across various fields, including materials science, drug discovery, and robotics, by enabling efficient exploration of design spaces and improved decision-making with limited data.