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Can we integrate spatial verification methods into neural-network loss
  functions for atmospheric science?

Can we integrate spatial verification methods into neural-network loss functions for atmospheric science?

21 March 2022
Ryan Lagerquist
I. Ebert‐Uphoff
ArXivPDFHTML

Papers citing "Can we integrate spatial verification methods into neural-network loss functions for atmospheric science?"

3 / 3 papers shown
Title
Inductive biases in deep learning models for weather prediction
Inductive biases in deep learning models for weather prediction
Jannik Thümmel
Matthias Karlbauer
S. Otte
C. Zarfl
Georg Martius
...
Thomas Scholten
Ulrich Friedrich
V. Wulfmeyer
B. Goswami
Martin Volker Butz
AI4CE
31
4
0
06 Apr 2023
Tools for Extracting Spatio-Temporal Patterns in Meteorological Image
  Sequences: From Feature Engineering to Attention-Based Neural Networks
Tools for Extracting Spatio-Temporal Patterns in Meteorological Image Sequences: From Feature Engineering to Attention-Based Neural Networks
A. S. Bansal
Yoonjin Lee
Kyle Hilburn
I. Ebert‐Uphoff
AI4TS
18
2
0
22 Oct 2022
Fourier Neural Operator for Parametric Partial Differential Equations
Fourier Neural Operator for Parametric Partial Differential Equations
Zong-Yi Li
Nikola B. Kovachki
Kamyar Azizzadenesheli
Burigede Liu
K. Bhattacharya
Andrew M. Stuart
Anima Anandkumar
AI4CE
203
2,254
0
18 Oct 2020
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