all AI news
A framework for deep learning emulation of numerical models with a case study in satellite remote sensing. (arXiv:1910.13408v2 [cs.LG] UPDATED)
cs.LG updates on arXiv.org arxiv.org
Numerical models based on physics represent the state-of-the-art in earth
system modeling and comprise our best tools for generating insights and
predictions. Despite rapid growth in computational power, the perceived need
for higher model resolutions overwhelms the latest-generation computers,
reducing the ability of modelers to generate simulations for understanding
parameter sensitivities and characterizing variability and uncertainty. Thus,
surrogate models are often developed to capture the essential attributes of the
full-blown numerical models. Recent successes of machine learning methods,
especially deep …
arxiv case study deep learning framework learning numerical remote satellite sensing study