Deep neural network models have become ubiquitous in recent years and have been
applied to nearly all areas of science, engineering, and industry. These models are …

Simulation models of multi‐sector systems are increasingly used to understand societal
resilience to climate and economic shocks and change. However, multi‐sector systems are …

COVID-19 has caused a serious impact on the global economy. Effectively stimulating
consumption has become a momentous mission in responding to the impact of the …

The potential contribution of ice sheets remains the largest source of uncertainty in
predicting sea‐level due to the limited predictive skill of numerical ice sheet models, yet …

A 3-D hybrid ice-sheet model is applied to the last deglacial retreat of the West Antarctic Ice
Sheet over the last∼ 20 000 yr. A large ensemble of 625 model runs is used to calibrate the …

The calibration of complex computer codes using uncertainty quantification (UQ) methods is
a rich area of statistical methodological development. When applying these techniques to …

Abstract Machine learning models can emulate chemical transport models, reducing
computational costs and enabling more experimentation. We developed emulators to predict …

Previous studies have interpreted Last Interglacial (LIG;∼ 129–116 ka) sea‐level estimates
in multiple different ways to calibrate projections of future Antarctic ice‐sheet (AIS) mass loss …

Deep neural network models have become ubiquitous in recent years, and have been
applied to nearly all areas of science, engineering, and industry. These models are …

There have been impressive advances in the physical and mathematical modelling of
complex physiological systems in the last few decades, with the potential to revolutionise …