420 research outputs found
Gaussian process surrogates for failure detection: a Bayesian experimental design approach
An important task of uncertainty quantification is to identify {the
probability of} undesired events, in particular, system failures, caused by
various sources of uncertainties. In this work we consider the construction of
Gaussian {process} surrogates for failure detection and failure probability
estimation. In particular, we consider the situation that the underlying
computer models are extremely expensive, and in this setting, determining the
sampling points in the state space is of essential importance. We formulate the
problem as an optimal experimental design for Bayesian inferences of the limit
state (i.e., the failure boundary) and propose an efficient numerical scheme to
solve the resulting optimization problem. In particular, the proposed
limit-state inference method is capable of determining multiple sampling points
at a time, and thus it is well suited for problems where multiple computer
simulations can be performed in parallel. The accuracy and performance of the
proposed method is demonstrated by both academic and practical examples
Neural network approximated Bayesian inference of edge electron density profiles at JET
A neural network (NN) has been trained on the inference of the edge electron density profiles from
measurements of the JET lithium beam emission spectroscopy (Li-BES) diagnostic. The novelty of the
approach resides in the fact that the network has been trained to be a fast surrogate model of an existing
Bayesian model of the diagnostic implemented within the Minerva framework. Previous work showed
the very first application of this method to an x-ray imaging diagnostic at the W7-X experiment, and it
was argued that the method was general enough that it may be applied to different physics systems.
Here, we try to show that the claim made there is valid. What makes the approach general and versatile
is the common definition of different models within the same framework. The network is tested on data
measured during several different pulses and the predictions compared to the results obtained with the
full model Bayesian inference. The NN analysis only requires tens of microseconds on a GPU
compared to the tens of minutes long full inference. Finally, in relation to what was presented in the
previous work, we demonstrate an improvement in the method of calculation of the network
uncertainties, achieved by using a state-of-the-art deep learning technique based on a variational
inference interpretation of the network training. The advantage of this calculation resides in the fact that
it relies on fewer assumptions, and no extra computation time is required besides the conventional
network evaluation time. This allows estimating the uncertainties also in real time applications.Comunidad Europea de la Energía Atómica. EURATOM - 2014-2018 y 2019-2020 - 63305
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