What is he best known for?
The fields of study he is best known for:
- Statistics
- Thermodynamics
- Mathematical analysis
His primary areas of investigation include Mechanics, Mathematical optimization, Porous medium, Ensemble Kalman filter and Permeability.
His Mathematical optimization research is multidisciplinary, relying on both Stochastic modelling, Inverse problem, Markov chain Monte Carlo, Random field and Applied mathematics.
In his study, Monte Carlo method and State is strongly linked to Stochastic process, which falls under the umbrella field of Applied mathematics.
His research in Porous medium intersects with topics in Statistical physics and Boundary value problem.
His Ensemble Kalman filter research is multidisciplinary, incorporating elements of Meteorology, Data assimilation, Simulation and Nonlinear system.
His Permeability research incorporates themes from Carbon sequestration, Mineralogy, Geomechanics and Finite volume method.
His most cited work include:
- Stochastic Methods for Flow in Porous Media: Coping with Uncertainties (319 citations)
- An efficient, high-order perturbation approach for flow in random porous media via Karhunen-Loève and polynomial expansions (268 citations)
- Data assimilation for transient flow in geologic formations via ensemble Kalman filter (268 citations)
What are the main themes of his work throughout his whole career to date?
The scientist’s investigation covers issues in Mathematical optimization, Monte Carlo method, Mechanics, Porous medium and Flow.
His Mathematical optimization research includes themes of Random variable, Uncertainty quantification, Polynomial chaos, Ensemble Kalman filter and Applied mathematics.
Dongxiao Zhang usually deals with Monte Carlo method and limits it to topics linked to Covariance and Mathematical analysis.
His Mechanics research focuses on Permeability and how it connects with Porosity, Soil science, Fluid dynamics and Carbon sequestration.
His study looks at the intersection of Porous medium and topics like Lattice Boltzmann methods with Mineralogy.
He has included themes like Hydraulic conductivity, Stochastic process and Stochastic modelling in his Flow study.
He most often published in these fields:
- Mathematical optimization (22.70%)
- Monte Carlo method (19.94%)
- Mechanics (16.87%)
What were the highlights of his more recent work (between 2018-2021)?
- Artificial intelligence (8.28%)
- Artificial neural network (6.44%)
- Deep learning (4.91%)
In recent papers he was focusing on the following fields of study:
Dongxiao Zhang spends much of his time researching Artificial intelligence, Artificial neural network, Deep learning, Partial differential equation and Algorithm.
His Artificial intelligence study combines topics in areas such as Machine learning, Subsurface flow and Pattern recognition.
The Artificial neural network study combines topics in areas such as Well logging, Training set, Mathematical optimization and Boundary value problem.
His Partial differential equation research includes elements of Dynamical systems theory, Conservation law, Domain decomposition methods and Groundwater flow equation.
His study in Domain decomposition methods is interdisciplinary in nature, drawing from both Discontinuity and Applied mathematics.
His work is dedicated to discovering how Algorithm, Genetic algorithm are connected with Parametric statistics and other disciplines.
Between 2018 and 2021, his most popular works were:
- Laboratory characterisation of fracture compressibility for coal and shale gas reservoir rocks: A review (35 citations)
- Deep learning of subsurface flow via theory-guided neural network (30 citations)
- Air-Sea Fluxes With a Focus on Heat and Momentum (28 citations)
In his most recent research, the most cited papers focused on:
- Statistics
- Thermodynamics
- Mathematical analysis
Dongxiao Zhang mostly deals with Oil shale, Artificial neural network, Artificial intelligence, Petroleum engineering and Partial differential equation.
His Oil shale study combines topics from a wide range of disciplines, such as Mineralogy, Methane and Thermodynamics.
While the research belongs to areas of Mineralogy, Dongxiao Zhang spends his time largely on the problem of Lead, intersecting his research to questions surrounding Anisotropy.
His Artificial neural network study integrates concerns from other disciplines, such as Boundary value problem, Deep learning, Mathematical optimization, Porous medium and Monte Carlo method.
Dongxiao Zhang interconnects Sedimentary rock and Phase in the investigation of issues within Petroleum engineering.
His Partial differential equation research integrates issues from Dynamical systems theory, Data assimilation and Groundwater flow equation.
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