| Digital rock segmentation for petrophysical analysis with reduced user bias using convolutional neural networks Y Niu, P Mostaghimi, M Shabaninejad, P Swietojanski, RT Armstrong Water Resources Research 56 (2), e2019WR026597, 2020 | 78 | 2020 |
| Coal permeability: gas slippage linked to permeability rebound Y Niu, P Mostaghimi, I Shikhov, Z Chen, RT Armstrong Fuel 215, 844-852, 2018 | 51 | 2018 |
| An innovative application of generative adversarial networks for physically accurate rock images with an unprecedented field of view Y Niu, Y Da Wang, P Mostaghimi, P Swietojanski, RT Armstrong Geophysical Research Letters 47 (23), e2020GL089029, 2020 | 44 | 2020 |
| Coal ash content estimation using fuzzy curves and ensemble neural networks for well log analysis I Siregar, Y Niu, P Mostaghimi, RT Armstrong International Journal of Coal Geology 181, 11-22, 2017 | 28 | 2017 |
| Deep learning of multi-resolution X-Ray micro-CT images for multi-scale modelling SJ Jackson, Y Niu, S Manoorkar, P Mostaghimi, RT Armstrong Physical Review Applied 17, 054046, 2022 | 25 | 2022 |
| Super-resolved segmentation of X-ray images of carbonate rocks using deep learning NJ Alqahtani, Y Niu, YD Wang, T Chung, Z Lanetc, A Zhuravljov, ... Transport in Porous Media 143 (2), 497-525, 2022 | 20 | 2022 |
| Geometrical-based generative adversarial network to enhance digital rock image quality Y Niu, Y Da Wang, P Mostaghimi, JE McClure, J Yin, RT Armstrong Physical Review Applied 15 (6), 064033, 2021 | 19 | 2021 |
| Paired and Unpaired Deep Learning Methods for Physically Accurate Super-Resolution Carbonate Rock Images Y Niu, SJ Jackson, N Alqahtani, P Mostaghimi, RT Armstrong Transport in Porous Media 144 (3), 825-847, 2022 | 12* | 2022 |
| Particle classification of iron ore sinter green bed mixtures by 3D X-ray microcomputed tomography and machine learning K Tang, Y Da Wang, Y Niu, TA Honeyands, D O’Dea, P Mostaghimi, ... Powder Technology 415, 118151, 2023 | 6 | 2023 |
| Effective permeability of an immiscible fluid in porous media determined from its geometric state F Al-Zubaidi, P Mostaghimi, Y Niu, RT Armstrong, G Mohammadi, ... Physical Review Fluids 8 (6), 064004, 2023 | 4 | 2023 |
| Dynamic X-ray micotomography of microfibrous cellulose liquid foams using deep learning SR Muin, PT Spicer, K Tang, Y Niu, M Hosseini, P Mostaghimi, ... Chemical Engineering Science 248, 117173, 2022 | 3 | 2022 |
| A Bayesian hierarchical model for the inference between metal grade with reduced variance: Case studies in porphyry Cu deposits Y Niu, M Lindsay, P Coghill, R Scalzo, L Zhang Geoscience Frontiers 15 (2), 101767, 2024 | 1 | 2024 |
| Applications of Physically Accurate Deep Learning for Processing Digital Rock Images Y Niu UNSW Sydney, 2022 | 1 | 2022 |
| Scaling Deep Learning for Material Imaging: A Pseudo-3d Model for Tera-Scale 3d Domain Transfer K Tang, R Armstrong, P Mostaghimi, Y Niu, Q Meyer, C Zhao, D Finegan, ... Available at SSRN 4808378, 2024 | | 2024 |
| Opportunities for Sustainability with Minerals Value Chain Integration M Lindsay, Y Niu, R Scalzo, SV Chambi, L Zhang, P Coghill, S Occhipinti 20th Annual Meeting of the Asia Oceania Geosciences Society, 1, 2023 | | 2023 |
| A Bayesian Hierarchical Model for Uncertainty Quantification of The Relationship Between Cu and Fe Grade in Porphyry Copper Deposit Y Niu, M Lindsay, P Coghill, L Zhang 26th World Mining Congress, Brisbane, Australia, 2458-2468, 2023 | | 2023 |
| PROPAGATING UNCERTAINTY THROUGH THE MINERALS PIPELINE: FROM REGIONAL PROSPECTIVITY TO THE (CONVEYOR) BELT M Lindsay, Y Niu, P Coghill, SA Occhipinti MinProXT 2022 Mineral Prospectivity and Exploration Targeting, 15, 2022 | | 2022 |
| Petrophysical analysis of coal samples from Gloucester Basin, New South Wales, Australia Y Niu The University of New South Wales, 2018 | | 2018 |
Ryan T. ArmstrongProfessor, University of New South WalesDirección de correo verificada de unsw.edu.au
