CIRA Guide to Custom Loss Functions for Neural Networks in Environmental Sciences -- Version 1

17 June 2021

Papers citing "CIRA Guide to Custom Loss Functions for Neural Networks in Environmental Sciences -- Version 1"

8 / 8 papers shown

Title
Custom Loss Functions in Fuel Moisture Modeling Jonathon Hirschi 24 0 0 03 Jan 2025
Kilometer-Scale Convection Allowing Model Emulation using Generative Diffusion Modeling Jaideep Pathak Y. Cohen Piyush Garg Peter Harrington Noah D. Brenowitz ... Morteza Mardani Arash Vahdat Shaoming Xu K. Kashinath Michael Pritchard DiffM AI4Cl 37 8 0 20 Aug 2024
Machine Learning Estimation of Maximum Vertical Velocity from Radar Randy J. Chase Amy McGovern Cameron Homeyer Peter Marinescu Corey K. Potvin 17 0 0 13 Oct 2023
Advancing Parsimonious Deep Learning Weather Prediction using the HEALPix Mesh Matthias Karlbauer Nathaniel Cresswell-Clay Dale Durran Raul A Moreno Thorsten Kurth Boris Bonev Noah D. Brenowitz Martin Volker Butz MDE 25 20 0 11 Sep 2023
Physics-constrained deep learning postprocessing of temperature and humidity Francesco Zanetta D. Nerini Tom Beucler M. Liniger AI4CE 11 5 0 07 Dec 2022
Increasing the accuracy and resolution of precipitation forecasts using deep generative models Ilan Price S. Rasp AI4Cl 37 50 0 23 Mar 2022
Can we integrate spatial verification methods into neural-network loss functions for atmospheric science? Ryan Lagerquist I. Ebert‐Uphoff 35 11 0 21 Mar 2022
Integrating Scientific Knowledge with Machine Learning for Engineering and Environmental Systems J. Willard X. Jia Shaoming Xu M. Steinbach Vipin Kumar AI4CE 91 387 0 10 Mar 2020