Publication date
October 2022
DOIAbstract
This includes the code and data used in the paper "A Physics-Incorporated Deep Learning Framework for Parameterization of Atmospheric Radiative Transfer"
This includes the code and data used in the paper "A Physics-Incorporated Deep Learning Framework for Parameterization of Atmospheric Radiative Transfer"
ARTS is a radiative transfer model for the millimeter and sub-millimeter spectral range. There are a...
GammaLearn is a collaborative project to apply deep learning to the analysis of low-level Imaging At...
Weighs and bias data for the artificial neural network models, and fluent deployable codes for imple...
This includes the code used in the paper "WRF-ML v1.0: A Bridge between WRF v4.3 and Machine Learnin...
We propose a novel machine learning algorithm for simulating radiative transfer. Our algorithm is ba...
This dataset is used for training the NN models in paper "A Radiative Transfer Deep Learning Model C...
Advances in deep learning have created new opportunities for improving traditional numerical models....
The radiative transfer equations are well known, but radiation parametrizations in atmospheric model...
This is a data package accompanying the paper "DeepGlow: an efficient neural-network emulator of phy...
Context. Computing spectra from 3D simulations of stellar atmospheres when allowing for departures f...
Introduction to the physics of atmospheric radiation and remote sensing including use of computer co...
Synthetic spectra calculated from model solar atmospheres are central to our understanding of the co...
Data associated with an upcoming paper on the use of neural networks (NN) to emulate a shortwave rad...
An atmospheric refractivity inversion method based on deep learning is introduced. Atmospheric duct ...
Cosmic radiation is an ionizing radiation produced when primary protons and a particles from outside...
ARTS is a radiative transfer model for the millimeter and sub-millimeter spectral range. There are a...
GammaLearn is a collaborative project to apply deep learning to the analysis of low-level Imaging At...
Weighs and bias data for the artificial neural network models, and fluent deployable codes for imple...
This includes the code used in the paper "WRF-ML v1.0: A Bridge between WRF v4.3 and Machine Learnin...
We propose a novel machine learning algorithm for simulating radiative transfer. Our algorithm is ba...
This dataset is used for training the NN models in paper "A Radiative Transfer Deep Learning Model C...
Advances in deep learning have created new opportunities for improving traditional numerical models....
The radiative transfer equations are well known, but radiation parametrizations in atmospheric model...
This is a data package accompanying the paper "DeepGlow: an efficient neural-network emulator of phy...
Context. Computing spectra from 3D simulations of stellar atmospheres when allowing for departures f...
Introduction to the physics of atmospheric radiation and remote sensing including use of computer co...
Synthetic spectra calculated from model solar atmospheres are central to our understanding of the co...
Data associated with an upcoming paper on the use of neural networks (NN) to emulate a shortwave rad...
An atmospheric refractivity inversion method based on deep learning is introduced. Atmospheric duct ...
Cosmic radiation is an ionizing radiation produced when primary protons and a particles from outside...
ARTS is a radiative transfer model for the millimeter and sub-millimeter spectral range. There are a...
GammaLearn is a collaborative project to apply deep learning to the analysis of low-level Imaging At...
Weighs and bias data for the artificial neural network models, and fluent deployable codes for imple...
Add to ORKG