Machine Learning-Based Surrogate Modeling for Urban Water Networks: Review and Future Research Directions

Surrogate models replace computationally expensive simulations of physically-based models to obtain accurate results at a fraction of the time. These surrogate models, also known as metamodels, have been employed for analysis, control, and optimization of water distribution and urban drainage systems. With the advent of machine learning (ML),...

Automatic tuning of photonic beamformers: A data-driven approach

Beamforming is a signal processing technique used in highly directional antennas. An array of antenna elements transmits the same signal, but with a different time delay for each element. By providing the right time delays for each antenna element, the whole array transmits a high-powered signal in one desired direction. This technique can be...