What is he best known for?
The fields of study he is best known for:
- Artificial intelligence
- Statistics
- Algorithm
Pejman Tahmasebi mostly deals with Algorithm, Artificial intelligence, Image, Porous medium and Raster graphics.
His Algorithm study incorporates themes from Visual comparison, Image based, Measure, Computer graphics and Categorical variable.
His Artificial intelligence study combines topics in areas such as Machine learning and Pattern recognition.
His research is interdisciplinary, bridging the disciplines of 3d model and Image.
The various areas that he examines in his Porous medium study include Function and Reconstruction method.
His research integrates issues of Flow, 3d image and Mineralogy in his study of Reconstruction method.
His most cited work include:
- Multiple-point geostatistical modeling based on the cross-correlation functions (172 citations)
- A hybrid neural networks-fuzzy logic-genetic algorithm for grade estimation (136 citations)
- Cross-correlation function for accurate reconstruction of heterogeneous media. (99 citations)
What are the main themes of his work throughout his whole career to date?
His primary scientific interests are in Porous medium, Algorithm, Artificial intelligence, Image and Permeability.
Porosity covers Pejman Tahmasebi research in Porous medium.
His Algorithm study integrates concerns from other disciplines, such as Flow, Hydrogeology, Image based, Cross-correlation and Function.
In his research, Pixel is intimately related to Data mining, which falls under the overarching field of Cross-correlation.
His Artificial intelligence study combines topics from a wide range of disciplines, such as Machine learning and Pattern recognition.
His Permeability study also includes fields such as
- Mineralogy together with Oil shale, Petrophysics, 3D reconstruction and Autocorrelation,
- Tortuosity that connect with fields like Fractal dimension and Fractal.
He most often published in these fields:
- Porous medium (46.24%)
- Algorithm (39.78%)
- Artificial intelligence (23.66%)
What were the highlights of his more recent work (between 2019-2021)?
- Porous medium (46.24%)
- Multiphase flow (9.68%)
- Artificial intelligence (23.66%)
In recent papers he was focusing on the following fields of study:
The scientist’s investigation covers issues in Porous medium, Multiphase flow, Artificial intelligence, Permeability and Mechanics.
His Porous medium research integrates issues from Field, Big data, Data science and Scale.
His studies deal with areas such as Machine learning and Kriging as well as Artificial intelligence.
His Permeability research is multidisciplinary, incorporating elements of Oil shale, Tortuosity, Mineralogy and Petroleum engineering.
Pejman Tahmasebi has included themes like Flow and Artificial neural network in his Deep learning study.
In his research, he undertakes multidisciplinary study on Quality and Algorithm.
Between 2019 and 2021, his most popular works were:
- Linking Morphology of Porous Media to Their Macroscopic Permeability by Deep Learning (34 citations)
- Machine learning in geo- and environmental sciences: From small to large scale (22 citations)
- Pore-Scale 3D Dynamic Modeling and Characterization of Shale Samples: Considering the Effects of Thermal Maturation (14 citations)
In his most recent research, the most cited papers focused on:
- Artificial intelligence
- Statistics
- Geometry
His main research concerns Artificial intelligence, Porous medium, Algorithm, Pixel and Flow.
His Artificial intelligence study frequently intersects with other fields, such as Computation.
Pejman Tahmasebi interconnects Field, Scale, Hydrogeology, Data science and Big data in the investigation of issues within Porous medium.
His work on Hybrid algorithm as part of his general Algorithm study is frequently connected to Reconstruction algorithm, thereby bridging the divide between different branches of science.
His research in Pixel intersects with topics in Image and Convolutional neural network.
His Deep learning research includes themes of Artificial neural network, Permeability, Relative permeability and Reconstruction method.
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