Improving Training For Scientific Machine Learning

Scientists are beginning to embrace the use of neural networks as a way to accelerate simulations in the world of scientific machine learning (SciML). Neural networks provide a mechanism to encode complex dependency structures, using many connected node layers to transform data into learned features to be used for a wide range of scientific tasks. Despite their popularity, neural networks can be difficult to train and employ on large-scale problems, which is why researchers are now exploring ways to make them more accessible and easier to use.