With the rapid development of artificial intelligence, machine learning is gradually becoming
popular for predictions in all walks of life. In meteorology, it is gradually competing with …

There is a growing consensus that solutions to complex science and engineering problems
require novel methodologies that are able to integrate traditional physics-based modeling …

There is a growing consensus that solutions to complex science and engineering problems
require novel methodologies that are able to integrate traditional physics-based modeling …

The recent development of machine learning (ML) and Deep Learning (DL) increases the
opportunities in all the sectors. ML is a significant tool that can be applied across many …

Physics-informed machine learning (PIML), referring to the combination of prior knowledge
of physics, which is the high level abstraction of natural phenomenons and human …

Progress within physical oceanography has been concurrent with the increasing
sophistication of tools available for its study. The incorporation of machine learning (ML) …

As the role played by statistical and computational sciences in climate and environmental
modelling and prediction becomes more important, Machine Learning researchers are …

At present, alloys have broad application prospects in heterogeneous catalysis, due to their
various catalytic active sites produced by their vast element combinations and complex …

Crop yield forecast models for barley, canola and spring wheat grown on the Canadian
Prairies were developed using vegetation indices derived from satellite data and machine …

Aims To examine and forecast the patterns of diabetes prevalence in synergy with obesity.
Methods Prophet models were employed to forecast the prevalence of diabetes and obesity …