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2020 – today
- 2024
[i3]Shengjuan Cai, Fangxin Fang, Vincent-Henri Peuch, Mihai Alexe, Ionel Michael Navon, Yanghua Wang:
Advancing operational PM2.5 forecasting with dual deep neural networks (D-DNet). CoRR abs/2406.19154 (2024)- 2023
- [j11]Meiling Cheng, Fangxin Fang, Ionel Michael Navon, Christopher C. Pain
:
Ensemble Kalman filter for GAN-ConvLSTM based long lead-time forecasting. J. Comput. Sci. 69: 102024 (2023) - 2021
- [j10]Yuepeng Wang, Xuemei Ding, Kun Hu, Fangxin Fang, I. M. Navon, Guang Lin:
Feasibility of DEIM for retrieving the initial field via dimensionality reduction. J. Comput. Phys. 429: 110005 (2021) - 2020
- [i2]M. Cheng, Fangxin Fang, Christopher C. Pain, I. M. Navon:
Data-driven modelling of nonlinear spatio-temporal fluid flows using a deep convolutional generative adversarial network. CoRR abs/2004.00707 (2020)
2010 – 2019
- 2019
- [c1]Rossella Arcucci, César Quilodrán Casas, Dunhui Xiao, Laetitia Mottet, Fangxin Fang, Pin Wu, Christopher C. Pain, Yi-Ke Guo:
A Domain Decomposition Reduced Order Model with Data Assimilation (DD-RODA). PARCO 2019: 189-198 - 2018
- [i1]Claire E. Heaney, Pablo Salinas, Fangxin Fang, Christopher C. Pain, I. M. Navon:
Goal-based sensitivity maps using time windows and ensemble perturbations. CoRR abs/1804.04457 (2018) - 2017
- [j9]Dunhui Xiao, Pan Yang, Fangxin Fang, Jiansheng Xiang, Christopher C. Pain, I. M. Navon, Min Chen:
A non-intrusive reduced-order model for compressible fluid and fractured solid coupling and its application to blasting. J. Comput. Phys. 330: 221-244 (2017) - 2016
- [j8]Alexandros Adam, Dimitris Pavlidis, James R. Percival, Pablo Salinas, Ziqing Xie, Fangxin Fang, Christopher C. Pain, A. H. Muggeridge, Matthew David Jackson:
Higher-order conservative interpolation between control-volume meshes: Application to advection and multiphase flow problems with dynamic mesh adaptivity. J. Comput. Phys. 321: 512-531 (2016) - [j7]Pan Yang, Jiansheng Xiang, Fangxin Fang, Dimitris Pavlidis, John-Paul Latham, Christopher C. Pain:
Modelling of fluid-structure interaction with multiphase viscous flows using an immersed-body method. J. Comput. Phys. 321: 571-592 (2016) - 2015
- [j6]Andrew G. Buchan, A. A. Calloo, Mark G. Goffin, Steven Dargaville, Fangxin Fang, Christopher C. Pain, Ionel Michael Navon:
A POD reduced order model for resolving angular direction in neutron/photon transport problems. J. Comput. Phys. 296: 138-157 (2015) - 2014
- [j5]Dunhui Xiao, Fangxin Fang, Andrew G. Buchan, Christopher C. Pain, Ionel Michael Navon, Juan Du, G. Hu:
Non-linear model reduction for the Navier-Stokes equations using residual DEIM method. J. Comput. Phys. 263: 1-18 (2014) - 2013
- [j4]Juan Du, Fangxin Fang, Christopher C. Pain, I. M. Navon, Jiang Zhu, David A. Ham:
POD reduced-order unstructured mesh modeling applied to 2D and 3D fluid flow. Comput. Math. Appl. 65(3): 362-379 (2013) - [j3]Juan Du, I. M. Navon, Jiang Zhu, Fangxin Fang, Aleksey K. Alekseev:
Reduced order modeling based on POD of a parabolized Navier-Stokes equations model II: Trust region POD 4D VAR data assimilation. Comput. Math. Appl. 65(3): 380-394 (2013) - [j2]Fangxin Fang, Christopher C. Pain, I. M. Navon, Ahmed H. Elsheikh, Juan Du, Dunhui Xiao:
Non-linear Petrov-Galerkin methods for reduced order hyperbolic equations and discontinuous finite element methods. J. Comput. Phys. 234: 540-559 (2013)
2000 – 2009
- 2006
- [j1]P. W. Power, Christopher C. Pain, M. D. Piggott, Fangxin Fang, Gerard J. Gorman, A. P. Umpleby, Anthony J. H. Goddard, I. M. Navon:
Adjoint A Posteriori Error Measures for Anisotropic Mesh Optimisation. Comput. Math. Appl. 52(8-9): 1213-1242 (2006)
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