Fangxin Fang

According to our database, Fangxin Fang authored at least 16 papers between 2006 and 2025.

Collaborative distances:

• Dijkstra number of five.
• Erdős number of four.

2025

An AI-driven framework for the prediction of personalised health response to air pollution.

Nazanin Zounemat Kermani, Sadjad Naderi, Claire H. Dilliway, Claire E. Heaney, Shrreya Behll, Boyang Chen, Hisham Abubakar-Waziri, Alexandra E. Porter, Marc Chadeau-Hyam, Fangxin Fang, Ian M. Adcock, Kian Fan Chung, Christopher C. Pain

CoRR, May, 2025

2024

Advancing operational PM2.5 forecasting with dual deep neural networks (D-DNet).

Shengjuan Cai, Fangxin Fang, Vincent-Henri Peuch, Mihai Alexe, Ionel Michael Navon, Yanghua Wang

CoRR, 2024

2023

Ensemble Kalman filter for GAN-ConvLSTM based long lead-time forecasting.

Meiling Cheng, Fangxin Fang, Ionel Michael Navon, Christopher C. Pain

J. Comput. Sci., May, 2023

2021

Feasibility of DEIM for retrieving the initial field via dimensionality reduction.

Yuepeng Wang, Xuemei Ding, Kun Hu, Fangxin Fang, I. M. Navon, Guang Lin

J. Comput. Phys., 2021

2020

Data-driven modelling of nonlinear spatio-temporal fluid flows using a deep convolutional generative adversarial network.

M. Cheng, Fangxin Fang, Christopher C. Pain, I. M. Navon

CoRR, 2020

2019

A Domain Decomposition Reduced Order Model with Data Assimilation (DD-RODA).

Rossella Arcucci, César Quilodrán Casas, Dunhui Xiao, Laetitia Mottet, Fangxin Fang, Pin Wu, Christopher C. Pain, Yi-Ke Guo

Proceedings of the Parallel Computing: Technology Trends, 2019

2018

Goal-based sensitivity maps using time windows and ensemble perturbations.

Claire E. Heaney, Pablo Salinas, Fangxin Fang, Christopher C. Pain, I. M. Navon

CoRR, 2018

2017

A non-intrusive reduced-order model for compressible fluid and fractured solid coupling and its application to blasting.

Dunhui Xiao, Pan Yang, Fangxin Fang, Jiansheng Xiang, Christopher C. Pain, I. M. Navon, Min Chen

J. Comput. Phys., 2017

2016

Modelling of fluid-structure interaction with multiphase viscous flows using an immersed-body method.

Pan Yang, Jiansheng Xiang, Fangxin Fang, Dimitris Pavlidis, John-Paul Latham, Christopher C. Pain

J. Comput. Phys., 2016

Higher-order conservative interpolation between control-volume meshes: Application to advection and multiphase flow problems with dynamic mesh adaptivity.

Alexandros Adam, Dimitris Pavlidis, James R. Percival, Pablo Salinas, Ziqing Xie, Fangxin Fang, Christopher C. Pain, A. H. Muggeridge, Matthew David Jackson

J. Comput. Phys., 2016

2015

A POD reduced order model for resolving angular direction in neutron/photon transport problems.

Andrew G. Buchan, A. A. Calloo, Mark G. Goffin, Steven Dargaville, Fangxin Fang, Christopher C. Pain, Ionel Michael Navon

J. Comput. Phys., 2015

2014

Non-linear model reduction for the Navier-Stokes equations using residual DEIM method.

Dunhui Xiao, Fangxin Fang, Andrew G. Buchan, Christopher C. Pain, Ionel Michael Navon, Juan Du, G. Hu

J. Comput. Phys., 2014

2013

Non-linear Petrov-Galerkin methods for reduced order hyperbolic equations and discontinuous finite element methods.

Fangxin Fang, Christopher C. Pain, I. M. Navon, Ahmed H. Elsheikh, Juan Du, Dunhui Xiao

J. Comput. Phys., 2013

Reduced order modeling based on POD of a parabolized Navier-Stokes equations model II: Trust region POD 4D VAR data assimilation.

Juan Du, I. M. Navon, Jiang Zhu, Fangxin Fang, Aleksey K. Alekseev

Comput. Math. Appl., 2013

POD reduced-order unstructured mesh modeling applied to 2D and 3D fluid flow.

Juan Du, Fangxin Fang, Christopher C. Pain, I. M. Navon, Jiang Zhu, David A. Ham

Comput. Math. Appl., 2013

2006

Adjoint A Posteriori Error Measures for Anisotropic Mesh Optimisation.

P. W. Power, Christopher C. Pain, M. D. Piggott, Fangxin Fang, Gerard J. Gorman, A. P. Umpleby, Anthony J. H. Goddard, I. M. Navon

Comput. Math. Appl., 2006