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Found 2 papers, 1 papers with code
Variable Selection and Regularization via Arbitrary Rectangle-range Generalized Elastic Net
We introduce the arbitrary rectangle-range generalized elastic net penalty method, abbreviated to ARGEN, for performing constrained variable selection and regularization in high-dimensional sparse linear models.
Learning to fail: Predicting fracture evolution in brittle material models using recurrent graph convolutional neural networks
Our methods use deep learning and train on simulation data from high-fidelity models, emulating the results of these models while avoiding the overwhelming computational demands associated with running a statistically significant sample of simulations.
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