Fangxin Fang

Fangxin Fang

Imperial College London

H-index: 35

Europe-United Kingdom

About Fangxin Fang

Fangxin Fang, With an exceptional h-index of 35 and a recent h-index of 29 (since 2020), a distinguished researcher at Imperial College London, specializes in the field of Data assimilation, machine learning and reduced order modelling, fluid modelling, adaptive meshes, air pollution.

His recent articles reflect a diverse array of research interests and contributions to the field:

A hybrid data‐driven and data assimilation method for spatiotemporal forecasting: PM2. 5 forecasting in China

Prioritising Actions for Improving Classroom Air Quality Based on the Analytic Hierarchy Process: Case Studies in China and the UK

A long short-term memory neural network-based error estimator for three-dimensional dynamically adaptive mesh generation

A non‐linear non‐intrusive reduced order model of fluid flow by auto‐encoder and self‐attention deep learning methods

Assessing uncertainty and heterogeneity in machine learning-based spatiotemporal ozone prediction in Beijing-Tianjin-Hebei region in China

A quantitative evaluation model of outdoor dynamic thermal comfort and adaptation: A year-long longitudinal field study

A reduced order with data assimilation model: Theory and practice

SARS-CoV2 in public spaces in West London, UK during COVID-19 pandemic

Fangxin Fang Information

University

Position

Senior Research Fellow Earth Science and Engineering

Citations(all)

3793

Citations(since 2020)

2390

Cited By

2358

hIndex(all)

35

hIndex(since 2020)

29

i10Index(all)

62

i10Index(since 2020)

47

Email

University Profile Page

Google Scholar

Fangxin Fang Skills & Research Interests

Data assimilation

machine learning and reduced order modelling

fluid modelling

adaptive meshes

air pollution

Top articles of Fangxin Fang

A hybrid data‐driven and data assimilation method for spatiotemporal forecasting: PM2. 5 forecasting in China

Journal of Advances in Modeling Earth Systems

2024/2

Prioritising Actions for Improving Classroom Air Quality Based on the Analytic Hierarchy Process: Case Studies in China and the UK

Indoor Air

2024/4/27

A long short-term memory neural network-based error estimator for three-dimensional dynamically adaptive mesh generation

Physics of Fluids

2023/10/1

A non‐linear non‐intrusive reduced order model of fluid flow by auto‐encoder and self‐attention deep learning methods

International Journal for Numerical Methods in Engineering

2023/7/15

Assessing uncertainty and heterogeneity in machine learning-based spatiotemporal ozone prediction in Beijing-Tianjin-Hebei region in China

Science of The Total Environment

2023/7/10

A quantitative evaluation model of outdoor dynamic thermal comfort and adaptation: A year-long longitudinal field study

Building and Environment

2023/6/1

A reduced order with data assimilation model: Theory and practice

Computers & Fluids

2023/5/15

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

Journal of Computational Science

2023/5/1

Nonstationary seismic–well tying with time-varying wavelets

Geophysics

2023/5/1

Fangxin Fang
Fangxin Fang

H-Index: 24

Modeling for understanding of coronavirus disease-2019 (COVID-19) spread and design of an isolation room in a hospital

Physics of Fluids

2023/2/1

Challenges and Prospects for Numerical Techniques in Atmospheric Modeling

2023/2

Fangxin Fang
Fangxin Fang

H-Index: 24

Active air monitoring for understanding the ventilation and infection risks of SARS-CoV-2 transmission in public indoor spaces

Atmosphere

2022/12/8

A non-intrusive reduced order model with transformer neural network and its application

Physics of Fluids

2022/11/1

SARS-CoV2 and air pollution interactions: airborne transmission and COVID-19

Molecular Frontiers Journal

2022/6/14

Spatio‐temporal hourly and daily ozone forecasting in China using a hybrid machine learning model: Autoencoder and generative adversarial networks

Journal of Advances in Modeling Earth Systems

2022/3

微分可能な流束表現を用いた o2o3 局所ガラーキン法

気象集誌. 第 2 輯

2022

The o2o3 Local Galerkin Method Using a Differentiable Flux Representation

Journal of the Meteorological Society of Japan. Ser. II

2022

Fangxin Fang
Fangxin Fang

H-Index: 24

Jiang Zhu
Jiang Zhu

H-Index: 27

Demonstration of a three-dimensional dynamically adaptive atmospheric dynamic framework for the simulation of mountain waves

Meteorology and Atmospheric Physics

2021/12

A real-time flow forecasting with deep convolutional generative adversarial network: Application to flooding event in Denmark

Physics of Fluids

2021/5/1

Meiling Cheng
Meiling Cheng

H-Index: 5

Fangxin Fang
Fangxin Fang

H-Index: 24

See List of Professors in Fangxin Fang University(Imperial College London)

Co-Authors

academic-engine