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D-Index & Metrics

Mechanical and Aerospace Engineering

D-Index
56
Citations
10423
World Ranking
863
National Ranking
49

Overview

Christopher C. Pain is affiliated with Imperial College London in the United Kingdom. Their research spans multiple areas within engineering and environmental science, with a strong focus on computational mechanics and related fields.

The primary fields of study for Christopher C. Pain include:

  • Engineering
  • Environmental Science

The subfields in which they have made contributions comprise:

  • Computational Mechanics
  • Statistical and Nonlinear Physics
  • Environmental Engineering
  • Atmospheric Science
  • Aerospace Engineering

The main topics covered in their work incorporate:

  • Model Reduction and Neural Networks
  • Meteorological Phenomena and Simulations
  • Lattice Boltzmann Simulation Studies
  • Air Quality and Health Impacts
  • Reservoir Engineering and Simulation Methods
  • Advanced Numerical Methods in Computational Mathematics
  • Wind and Air Flow Studies

Christopher C. Pain has published frequently in a number of scientific venues, notable among these are:

  • arXiv (Cornell University)
  • Physics of Fluids
  • SSRN Electronic Journal
  • Computer Methods in Applied Mechanics and Engineering
  • The Science of The Total Environment

Some of their recent published papers include:

  • "Long lead-time daily and monthly streamflow forecasting using machine learning methods," 2020, Journal of Hydrology
  • "Data-driven modelling of nonlinear spatio-temporal fluid flows using a deep convolutional generative adversarial network," 2020, Computer Methods in Applied Mechanics and Engineering
  • "An overview of methods of fine and ultrafine particle collection for physicochemical characterisation and toxicity assessments," 2020, The Science of The Total Environment
  • "The ventilation of buildings and other mitigating measures for COVID-19: a focus on wintertime," 2021, Bristol Research (University of Bristol)
  • "Generalised Latent Assimilation in Heterogeneous Reduced Spaces with Machine Learning Surrogate Models," 2022, Journal of Scientific Computing

Frequent collaborators in their research include:

  • Claire E. Heaney
  • Pablo Salinas
  • Rossella Arcucci
  • F. Fang
  • Yike Guo

Best Publications

  • Non-linear regimes of fluid flow in rock fractures

    Robert W Zimmerman;Azzan Al-Yaarubi;Chris C Pain;Carlos A Grattoni

  • Tetrahedral mesh optimisation and adaptivity for steady-state and transient finite element calculations

    C.C. Pain;A.P. Umpleby;C.R.E. de Oliveira;A.J.H. Goddard

  • Verification and validation of a coarse grain model of the DEM in a bubbling fluidized bed

    Mikio Sakai;Minami Abe;Yusuke Shigeto;Shin Mizutani

  • Model identification of reduced order fluid dynamics systems using deep learning

    Z. Wang;Dunhui Xiao;F. Fang;R. Govindan

  • Three-dimensional unstructured mesh ocean modelling

    C.C. Pain;M.D. Piggott;A.J.H. Goddard;F. Fang

  • A new computational framework for multi-scale ocean modelling based on adapting unstructured meshes†

    M. D. Piggott;G. J. Gorman;C. C. Pain;P. A. Allison

  • Non-intrusive reduced-order modelling of the Navier-Stokes equations based on RBF interpolation

    D. Xiao;D. Xiao;F. Fang;C. Pain;G. Hu

  • A study of bubbling and slugging fluidised beds using the two-fluid granular temperature model

    C.C. Pain;S. Mansoorzadeh;C.R.E. de Oliveira

  • Non-intrusive reduced order modelling of the Navier-Stokes equations

    Dunhui Xiao;Dunhui Xiao;F. Fang;A.G. Buchan;C.C. Pain

  • Study on a large-scale discrete element model for fine particles in a fluidized bed

    Mikio Sakai;Hiroyuki Takahashi;Christopher C. Pain;John-Paul Latham

  • How tall buildings affect turbulent air flows and dispersion of pollution within a neighbourhood.

    Elsa Aristodemou;Elsa Aristodemou;Luz Maria Boganegra;Laetitia Mottet;Dimitrios Pavlidis

  • A reduced order model for turbulent flows in the urban environment using machine learning

    Dunhui Xiao;Dunhui Xiao;C.E. Heaney;L. Mottet;L. Mottet;F. Fang

  • A parameterized non-intrusive reduced order model and error analysis for general time-dependent nonlinear partial differential equations and its applications

    D. Xiao;F. Fang;C.C. Pain;I.M. Navon

  • A Simple Model for Deviations from the Cubic Law for a Fracture Undergoing Dilation or Closure

    Sourith Sisavath;Azzan Al-Yaarubi;Chris C. Pain;Robert W. Zimmerman

  • Non-intrusive reduced order modelling of fluid–structure interactions

    D. Xiao;D. Xiao;P. Yang;F. Fang;J. Xiang

  • Data-driven reduced order model with temporal convolutional neural network

    Pin Wu;Pin Wu;Junwu Sun;Xuting Chang;Wenjie Zhang

  • Anisotropic mesh adaptivity for multi-scale ocean modelling.

    M. D. Piggott;P. E. Farrell;C. R. Wilson;G. J. Gorman

  • Fluid flow monitoring in oil fields using downhole measurements of electrokinetic potential

    Jon H. Saunders;Matthew D. Jackson;Christopher C. Pain

  • Reservoir Modeling for Flow Simulation by Use of Surfaces, Adaptive Unstructured Meshes, and an Overlapping-Control-Volume Finite-Element Method

    Matthew D. Jackson;James R. Percival;Peyman Mostaghimi;Brendan Tollit

  • A mixed discontinuous/continuous finite element pair for shallow-water ocean modelling

    Colin J. Cotter;David A. Ham;Christopher C. Pain

  • Anisotropic resistivity inversion

    Christopher C Pain;Jörg V Herwanger;Jonathan H Saunders;Michael H Worthington

  • Non-linear Petrov–Galerkin methods for reduced order modelling of the Navier–Stokes equations using a mixed finite element pair

    D. Xiao;D. Xiao;F. Fang;J. Du;C. C. Pain

Frequent Co-Authors

Matthew D. Jackson
Matthew D. Jackson Imperial College London
Omar Matar
Omar Matar Imperial College London
I. M. Navon
I. M. Navon Florida State University
Yike Guo
Yike Guo Hong Kong Baptist University
Peter A. Allison
Peter A. Allison Imperial College London
Peyman Mostaghimi
Peyman Mostaghimi University of New South Wales
Gary J. Hampson
Gary J. Hampson Imperial College London
Martin J. Blunt
Martin J. Blunt Imperial College London
Alan Robins
Alan Robins University of Surrey
Paul Linden
Paul Linden University of Cambridge

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