Scientific machine learning has increasingly focused on surrogates for solving partial differential equations, modeling dynamical systems, and learning physical phenomena from data. These surrogates offer efficient and scalable alternatives to traditional methods, with significant potential across diverse applications. However, challenges persist in ensuring stability and providing robust theoretical guarantees. This workshop will explore recent advances in surrogate modeling, emphasizing dimension reduction, effective training strategies, and methods that enhance accuracy and theoretical rigor.