Local Surrogate

Local surrogate models are simplified representations of complex, often black-box, systems used to approximate their behavior in a specific region. Research currently focuses on improving their accuracy and robustness, particularly addressing challenges like sensitivity to input variations and the need for efficient training, often employing techniques like Bayesian optimization and distribution matching. These models find applications across diverse fields, from explaining the behavior of machine learning models and optimizing expensive simulations to interpreting temporal data and accelerating materials discovery, ultimately enhancing interpretability, efficiency, and decision-making in various scientific and engineering domains.