Probabilistic deep learning with variational inference
Typ
Examensarbete för masterexamen
Program
Engineering mathematics and computational science (MPENM), MSc
Publicerad
2020
Författare
Hegnar, Eva
Modellbyggare
Tidskriftstitel
ISSN
Volymtitel
Utgivare
Sammanfattning
Deep neural networks are used in the petroleum industry to model gas and oil rate.
To optimise the production, the uncertainty of the network predictions is desirable.
The neural network weights are equipped with prior distributions to be able to
quantify the uncertainty of the model predictions within the Bayesian paradigm.
To obtain a numerically feasible procedure two different approaches of variational
inference are used and compared; black box variational inference and variational
inference using the reparameterisation trick. Both approaches are applied to real
measurements of gas and oil rate, which were given by Solution Seeker, a company
providing production optimisation to the petroleum industry. The results show a
more stable convergence using the reparameterisation trick. The uncertainty in
predictions is possible to be quantified using variational inference but setting a
proper prior distribution is difficult.
Beskrivning
Ämne/nyckelord
deep neural network, Bayesian inference, variational inference, black box variational inference, reparameterisation trick, probabilistic modelling, production optimisation, flow rate estimation