What is he best known for?
The fields of study he is best known for:
- Thermodynamics
- Statistics
- Mechanics
Christopher C. Pain mainly focuses on Finite element method, Mechanics, Mathematical analysis, Representation and Geometry.
His Finite element method research is multidisciplinary, relying on both Computational fluid dynamics, Inverse problem, Biot number, Simulation and Discretization.
His Flow and Reynolds number study in the realm of Mechanics connects with subjects such as Materials science.
The Mathematical analysis study combines topics in areas such as Covariance, Petrov–Galerkin method, Residual, Extended finite element method and Discontinuous Galerkin method.
Christopher C. Pain studied Representation and Computational science that intersect with Mesh generation, Adaptive resolution, Unstructured mesh and Anisotropic meshes.
His Geometry study combines topics in areas such as Boltzmann equation and Topology.
His most cited work include:
- Non-linear regimes of fluid flow in rock fractures (255 citations)
- Tetrahedral mesh optimisation and adaptivity for steady-state and transient finite element calculations (240 citations)
- Three-dimensional unstructured mesh ocean modelling (152 citations)
What are the main themes of his work throughout his whole career to date?
Christopher C. Pain mainly investigates Finite element method, Mechanics, Materials science, Polygon mesh and Mathematical analysis.
The concepts of his Finite element method study are interwoven with issues in Computational fluid dynamics, Boltzmann equation, Applied mathematics, Control volume and Discretization.
His studies deal with areas such as Wavelet, Convection–diffusion equation and Mathematical optimization as well as Discretization.
His work on Porous medium expands to the thematically related Mechanics.
His Polygon mesh research is multidisciplinary, incorporating perspectives in Algorithm and Computational science.
His Mathematical analysis study combines topics from a wide range of disciplines, such as Geometry, Mixed finite element method, Extended finite element method and Discontinuous Galerkin method.
He most often published in these fields:
- Finite element method (33.02%)
- Mechanics (29.84%)
- Materials science (14.60%)
What were the highlights of his more recent work (between 2018-2021)?
- Finite element method (33.02%)
- Algorithm (10.16%)
- Artificial intelligence (4.13%)
In recent papers he was focusing on the following fields of study:
The scientist’s investigation covers issues in Finite element method, Algorithm, Artificial intelligence, Polygon mesh and Mechanics.
By researching both Finite element method and Materials science, Christopher C. Pain produces research that crosses academic boundaries.
The various areas that Christopher C. Pain examines in his Algorithm study include Fluid dynamics, Autoencoder, Data assimilation and Nonlinear system.
His study in the field of Deep learning and Artificial neural network is also linked to topics like Scale and Water resources.
His research integrates issues of Multiphase flow, Static mesh, Network model, Heat flux and Computational science in his study of Polygon mesh.
Within one scientific family, he focuses on topics pertaining to Fluid–structure interaction under Mechanics, and may sometimes address concerns connected to Chemical physics, Porosity and Vortex-induced vibration.
Between 2018 and 2021, his most popular works were:
- Optimal reduced space for Variational Data Assimilation (25 citations)
- A reduced order model for turbulent flows in the urban environment using machine learning (23 citations)
- A domain decomposition non-intrusive reduced order model for turbulent flows (21 citations)
In his most recent research, the most cited papers focused on:
- Thermodynamics
- Statistics
- Artificial intelligence
Christopher C. Pain focuses on Flow, Finite element method, Artificial intelligence, Fluid dynamics and Basis function.
His studies in Flow integrate themes in fields like Data-driven and Computer simulation.
His work deals with themes such as Polygon mesh, Computational science, Angular resolution, Multigrid method and Wavelet, which intersect with Finite element method.
His work on Deep learning and Artificial neural network as part of general Artificial intelligence study is frequently linked to Scale, bridging the gap between disciplines.
His Fluid dynamics research includes themes of Boundary value problem, Domain, Mathematical analysis and Domain decomposition methods.
His Basis function research incorporates elements of Algorithm and Reduction.
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