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Found 4 papers, 0 papers with code
Robust Bayesian Target Value Optimization
We consider the problem of finding an input to a stochastic black box function such that the scalar output of the black box function is as close as possible to a target value in the sense of the expected squared error.
A connection between probability, physics and neural networks
The central limit theorem then suggests that NNs can be constructed to obey a physical law by choosing the activation functions such that they match a particular kernel in the infinite-width limit.
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.
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
