As materials researchers increasingly embrace machine-learning (ML) methods, it is natural
to wonder what lessons can be learned from other fields undergoing similar developments …

Bayesian optimisation is a statistical method that efficiently models and optimises expensive
“black-box” functions. This review considers the application of Bayesian optimisation to …

Large-scale data centers run latency-critical jobs with quality-of-service (QoS) requirements,
and throughput-oriented background jobs, which need to achieve high perfor-mance …

Experimental solid mechanics is at a pivotal point where machine learning (ML) approaches
are rapidly proliferating into the discovery process due to significant advances in data …

Bayesian optimization is known to be difficult to scale to high dimensions, because the
acquisition step requires solving a non-convex optimization problem in the same search …

In recent years self-driving vehicles have become more commonplace on public roads, with
the promise of bringing safety and efficiency to modern transportation systems. Increasing …

In some real-world applications, the optimization problems may involve multiple design
stages. At each design stage, the objective is incrementally modified by incorporating more …