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Found 4 papers, 0 papers with code
A priori compression of convolutional neural networks for wave simulators
The time needed to make predictions or time required for training using the original Convolutional Neural Networks model would be cut significantly if there were fewer parameters to deal with.
Uncertainty quantification in a mechanical submodel driven by a Wasserstein-GAN
An architecture that support a random component is necessary for the construction of the stochastic model of the physical system for non-parametric uncertainties, since the goal is to learn the underlying probabilistic distribution of uncertainty in the data.
Uncertainty quantification for industrial design using dictionaries of reduced order models
The main contribution of this work is the application of the complete workflow to a real-life industrial model of an elastoviscoplastic high-pressure turbine blade subjected to thermal, centrifugal and pressure loadings, for the quantification of the uncertainty on dual quantities (such as the accumulated plastic strain and the stress tensor), generated by the uncertainty on the temperature loading field.
Data augmentation and feature selection for automatic model recommendation in computational physics
Classification algorithms have recently found applications in computational physics for the selection of numerical methods or models adapted to the environment and the state of the physical system.
