2 papers
physics.ao-ph2022
Exploring Randomly Wired Neural Networks for Climate Model Emulation
William Yik, Sam J. Silva, Andrew Geiss +1
Exploring the climate impacts of various anthropogenic emissions scenarios is key to making informed decisions for climate change mitigation and adaptation. State-of-the-art Earth…
eess.IV2020
Strict Enforcement of Conservation Laws and Invertibility in CNN-Based Super Resolution for Scientific Datasets
Andrew Geiss, Joseph C. Hardin
Recently, deep Convolutional Neural Networks (CNNs) have revolutionized image super-resolution (SR), dramatically outperforming past methods for enhancing image resolution. They co…