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Found 3 papers, 2 papers with code
Statistical treatment of convolutional neural network super-resolution of inland surface wind for subgrid-scale variability quantification
For each downscaling factor, we consider three CNN configurations that generate super-resolved predictions of fine-scale wind speed, which take between 1 to 3 input fields: coarse wind speed, fine-scale topography, and diurnal cycle.
Neural Networks for Parameter Estimation in Intractable Models
We propose to use deep learning to estimate parameters in statistical models when standard likelihood estimation methods are computationally infeasible.
Parameter Estimation with Dense and Convolutional Neural Networks Applied to the FitzHugh-Nagumo ODE
We employ the neural networks to approximate reconstruction maps for model parameter estimation from observational data, where the data comes from the solution of the ODE and takes the form of a time series representing dynamically spiking membrane potential of a biological neuron.
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