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2010 – 2019
- 2019
[i5]Lukas Mosser, Olivier Dubrule, Martin J. Blunt:
DeepFlow: History Matching in the Space of Deep Generative Models. CoRR abs/1905.05749 (2019)- 2018
- [j4]Mosayeb Shams, Ali Q. Raeini
, Martin J. Blunt, Branko Bijeljic:
A numerical model of two-phase flow at the micro-scale using the volume-of-fluid method. J. Comput. Phys. 357: 159-182 (2018) - [i4]Lukas Mosser, Olivier Dubrule, Martin J. Blunt:
Conditioning of three-dimensional generative adversarial networks for pore and reservoir-scale models. CoRR abs/1802.05622 (2018) - [i3]Lukas Mosser, Olivier Dubrule, Martin J. Blunt:
Stochastic seismic waveform inversion using generative adversarial networks as a geological prior. CoRR abs/1806.03720 (2018) - 2017
- [i2]Lukas Mosser, Olivier Dubrule, Martin J. Blunt:
Reconstruction of three-dimensional porous media using generative adversarial neural networks. CoRR abs/1704.03225 (2017) - [i1]Lukas Mosser, Olivier Dubrule, Martin J. Blunt:
Stochastic reconstruction of an oolitic limestone by generative adversarial networks. CoRR abs/1712.02854 (2017) - 2015
- [j3]Ahmad S. Abushaikha, Martin J. Blunt, Olivier R. Gosselin, Christopher C. Pain, Matthew David Jackson:
Interface control volume finite element method for modelling multi-phase fluid flow in highly heterogeneous and fractured reservoirs. J. Comput. Phys. 298: 41-61 (2015) - 2012
- [j2]Ali Q. Raeini, Martin J. Blunt, Branko Bijeljic:
Modelling two-phase flow in porous media at the pore scale using the volume-of-fluid method. J. Comput. Phys. 231(17): 5653-5668 (2012) - [j1]N. Al-Bulushi, P. R. King, Martin J. Blunt, M. A. Kraaijveld:
Artificial neural networks workflow and its application in the petroleum industry. Neural Comput. Appl. 21(3): 409-421 (2012)
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