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Found 3 papers, 1 papers with code
Pruning deep neural networks generates a sparse, bio-inspired nonlinear controller for insect flight
This bio-inspired approach allows us to leverage network pruning to optimally sparsify a DNN architecture in order to perform flight tasks with as few neural connections as possible, however, there are limits to sparsification.
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.
