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Found 4 papers, 2 papers with code
A multiscale and multicriteria Generative Adversarial Network to synthesize 1-dimensional turbulent fields
The model is a Generative Adversarial Network with multiple multiscale optimization criteria.
Neural network based generation of a 1-dimensional stochastic field with turbulent velocity statistics
We define and study a fully-convolutional neural network stochastic model, NN-Turb, which generates a 1-dimensional field with some turbulent velocity statistics.
Deep learning for Lagrangian drift simulation at the sea surface
We address Lagrangian drift simulation in geophysical dynamics and explore deep learning approaches to overcome known limitations of state-of-the-art model-based and Markovian approaches in terms of computational complexity and error propagation.
Convolutional Neural Network Modelling for MODIS Land Surface Temperature Super-Resolution
Our proposed network is a modified version of U-Net architecture, which aims at super-resolving the input LST image from 1Km to 250m per pixel.
