Physically Interpretable Neural Networks for the Geosciences:
Applications to Earth System Variability

Physically Interpretable Neural Networks for the Geosciences: Applications to Earth System Variability

4 December 2019

I. Ebert‐Uphoff

Papers citing "Physically Interpretable Neural Networks for the Geosciences: Applications to Earth System Variability"

11 / 11 papers shown

Title
Physically Constrained Generative Adversarial Networks for Improving Precipitation Fields from Earth System Models P. Hess Markus Drüke S. Petri Felix M. Strnad Niklas Boers 33 60 0 03 Jan 2025
Tackling the Accuracy-Interpretability Trade-off in a Hierarchy of Machine Learning Models for the Prediction of Extreme Heatwaves Alessandro Lovo Amaury Lancelin Corentin Herbert Freddy Bouchet AI4CE 23 0 0 01 Oct 2024
ClimDetect: A Benchmark Dataset for Climate Change Detection and Attribution Sungduk Yu Brian L. White Anahita Bhiwandiwalla Musashi Hinck M. L. Olson Tung Nguyen Vasudev Lal Tung Nguyen Vasudev Lal 34 0 0 28 Aug 2024
Interpretable Climate Change Modeling With Progressive Cascade Networks Chuck Anderson Jason Stock D. Anderson AI4Cl AI4CE 27 0 0 12 May 2022
AtmoDist: Self-supervised Representation Learning for Atmospheric Dynamics Sebastian Hoffmann C. Lessig AI4Cl 24 8 0 02 Feb 2022
Combining data assimilation and machine learning to estimate parameters of a convective-scale model Stefanie Legler T. Janjić 21 18 0 07 Sep 2021
Loosely Conditioned Emulation of Global Climate Models With Generative Adversarial Networks Alexis Ayala Christopher Drazic Brian Hutchinson Ben Kravitz Claudia Tebaldi GAN AI4Cl AI4CE 17 6 0 29 Apr 2021
Bridging observation, theory and numerical simulation of the ocean using Machine Learning Maike Sonnewald Redouane Lguensat Daniel C. Jones P. Dueben J. Brajard Venkatramani Balaji AI4Cl AI4CE 38 99 0 26 Apr 2021
Controlled abstention neural networks for identifying skillful predictions for classification problems E. Barnes R. Barnes 13 8 0 16 Apr 2021
Neural Network Attribution Methods for Problems in Geoscience: A Novel Synthetic Benchmark Dataset Antonios Mamalakis I. Ebert‐Uphoff E. Barnes OOD 20 75 0 18 Mar 2021
Methods for Interpreting and Understanding Deep Neural Networks G. Montavon Wojciech Samek K. Müller FaML 234 2,233 0 24 Jun 2017