In recent years, the frequency of weather-related natural disasters—cyclones, torrential rains, floods—has increased as a consequence of global warming. These disasters cause billions of dollars in ...

Abstract: We present a methodology based on Bayesian optimization (BO) for the performance enhancement of a dual-input Doherty power amplifier (DIDPA) operating at 24 GHz under wideband modulation.

When Meta broke ground last year on its data center in Rosemount, Minn., about 15 miles south of Minneapolis and St. Paul, the social media giant faced a concrete paradox: the material’s carbon burden ...

Abstract: In vehicle trajectory prediction, traditional methods like Kalman filtering often rely heavily on user expertise and prior knowledge, while newer deep learning approaches, such as Long Short ...

Next to the primary optimization objectives, scientific optimization problems often contain a series of subordinate objectives, which can be expressed as preferences over either the outputs of an ...

The Bayesian approach to statistical inference and other data analysis tasks gets its name from Bayes’s theorem (BT). BT specifies that a posterior probability for a hypothesis concerning a data ...

The Sargent Centre is delighted to announce we are hosting our third edition summer school, on the topic of Data-driven optimisation: Bayesian optimisation. The school will consist of preliminary ...

Muons have broad applications in fundamental science such as material science, chemistry, biology, and nuclear physics [1 – 7], which are produced mainly through the proton-nucleon reactions driven by ...