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Found 4 papers, 0 papers with code
Stochastic Modeling of Inhomogeneities in the Aortic Wall and Uncertainty Quantification using a Bayesian Encoder-Decoder Surrogate
Then to assess the uncertainty in the output stress distribution due to this stochastic constitutive model, a convolutional neural network, specifically a Bayesian encoder-decoder, was used as a surrogate model that maps the random input fields to the output stress distribution obtained from the FE analysis.
LaBH$_{8}$: the first high-T$_{c}$ low-pressure superhydride
In the last five years a large number of new high-temperature superconductors have been predicted and experimentally discovered among hydrogen-rich crystals, at pressures which are way too high to meet any practical application.
Superconductivity
Importance of feature engineering and database selection in a machine learning model: A case study on carbon crystal structures
In this work we seek to understand in depth the effect that the choice of features and the properties of the database have on a machine learning application.
Bayesian Surrogate Analysis and Uncertainty Propagation
In the process, the contribution of the uncertainties of the surrogate itself to the simulation output uncertainties are usually neglected.
Methodology Data Analysis, Statistics and Probability
