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Found 7 papers, 1 papers with code
Combining Statistical Depth and Fermat Distance for Uncertainty Quantification
The method is applicable to any classification model as it is applied directly in feature space at test time and does not intervene in training process.
A Block-Coordinate Approach of Multi-level Optimization with an Application to Physics-Informed Neural Networks
Multi-level methods are widely used for the solution of large-scale problems, because of their computational advantages and exploitation of the complementarity between the involved sub-problems.
A coarse space acceleration of deep-DDM
We present an extension of this method that relies on the use of a coarse space correction, similarly to what is done in traditional DDM solvers.
Multilevel physics informed neural networks (MPINNs)
In this paper we introduce multilevel physics informed neural networks (MPINNs).
Latent Space Data Assimilation by using Deep Learning
Performing Data Assimilation (DA) at a low cost is of prime concern in Earth system modeling, particularly at the time of big data where huge quantities of observations are available.
Data Assimilation Networks
Data assimilation (DA) aims at forecasting the state of a dynamical system by combining a mathematical representation of the system with noisy observations taking into account their uncertainties.
On the approximation of the solution of partial differential equations by artificial neural networks trained by a multilevel Levenberg-Marquardt method
Here a feedforward neural network is used to approximate the solution of the partial differential equation.
