Conference Agenda

This talk presents various physics-informed ML methods to search high-dimensional continuous spaces in a sample efficient way, with a focus on urban mobility applications. We present advances in three areas: (i) sample-efficient dimensionality reduction methods, (ii) sample-efficient simulation-based optimization algorithms, (i) variance reduction methods for gradient estimation. We present case studies based on various metropolitan areas. We identify and discuss research opportunities and challenges in the fields of simulation-based optimization and machine learning as applied to urban mobility problems.