Distributed deep learning has emerged as an essential approach for training large-scale deep neural networks by utilising multiple computational nodes. This methodology partitions the workload either ...

Parallel computing for differential equations has emerged as a critical field in computational science, enabling the efficient simulation of complex physical systems governed by ordinary and partial ...