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Found 5 papers, 1 papers with code
Accelerating hypersonic reentry simulations using deep learning-based hybridization (with guarantees)
To tackle this trade-off, we design a hybrid simulation code coupling a traditional fluid dynamic solver with a neural network approximating the chemical reactions.
Goal-Oriented Sensitivity Analysis of Hyperparameters in Deep Learning
In this work, we study the use of goal-oriented sensitivity analysis, based on the Hilbert-Schmidt Independence Criterion (HSIC), for hyperparameter analysis and optimization.
A Taylor Based Sampling Scheme for Machine Learning in Computational Physics
Machine Learning (ML) is increasingly used to construct surrogate models for physical simulations.
Leveraging Local Variation in Data: Sampling and Weighting Schemes for Supervised Deep Learning
In the context of supervised learning of a function by a neural network, we claim and empirically verify that the neural network yields better results when the distribution of the data set focuses on regions where the function to learn is steep.
Variance Based Sample Weighting for Supervised Learning
In the context of supervised learning of a function by a Neural Network (NN), we claim and empirically justify that a NN yields better results when the distribution of the data set focuses on regions where the function to learn is steeper.
