What is he best known for?
The fields of study he is best known for:
- Statistics
- Thermodynamics
- Mathematical analysis
His primary scientific interests are in Mathematical optimization, Applied mathematics, Statistics, Aquifer and Environmental chemistry.
Peter K. Kitanidis interconnects State space and Bayesian inference in the investigation of issues within Mathematical optimization.
His study in Applied mathematics is interdisciplinary in nature, drawing from both Covariance, Inverse problem and Variogram.
His Covariance study combines topics in areas such as Estimation theory and Covariance matrix.
His biological study spans a wide range of topics, including Soil science, Mixing and Problem domain.
The concepts of his Environmental chemistry study are interwoven with issues in Biostimulation, Bioremediation and Environmental engineering.
His most cited work include:
- Introduction to Geostatistics: Applications in Hydrogeology (725 citations)
- A geostatistical approach to the inverse problem in groundwater modeling (steady state) and one‐dimensional simulations (442 citations)
- Quasi‐Linear Geostatistical Theory for Inversing (408 citations)
What are the main themes of his work throughout his whole career to date?
Peter K. Kitanidis mainly investigates Mathematical optimization, Aquifer, Applied mathematics, Hydrology and Inverse problem.
His studies examine the connections between Mathematical optimization and genetics, as well as such issues in Kalman filter, with regards to Data assimilation.
His Aquifer study frequently links to adjacent areas such as Soil science.
Peter K. Kitanidis studied Applied mathematics and Covariance that intersect with Covariance matrix.
His Hydrology research incorporates elements of Mass transfer, Dilution and Flow.
His studies deal with areas such as Uncertainty quantification, Algorithm, Inverse and Bayesian probability as well as Inverse problem.
He most often published in these fields:
- Mathematical optimization (16.46%)
- Aquifer (15.19%)
- Applied mathematics (12.66%)
What were the highlights of his more recent work (between 2011-2021)?
- Aquifer (15.19%)
- Inverse problem (11.39%)
- Algorithm (7.91%)
In recent papers he was focusing on the following fields of study:
Peter K. Kitanidis mostly deals with Aquifer, Inverse problem, Algorithm, Hydraulic tomography and Applied mathematics.
His work on Groundwater recharge as part of general Aquifer study is frequently linked to Field, bridging the gap between disciplines.
Peter K. Kitanidis combines subjects such as Uncertainty quantification, Inverse, Covariance matrix, Jacobian matrix and determinant and Hierarchical matrix with his study of Inverse problem.
His Covariance matrix study also includes
- Covariance that connect with fields like Mathematical optimization,
- Hessian matrix that connect with fields like Covariance function.
Peter K. Kitanidis has researched Applied mathematics in several fields, including Geostatistics, Krylov subspace and Square root.
His study looks at the relationship between Geotechnical engineering and fields such as Mechanics, as well as how they intersect with chemical problems.
Between 2011 and 2021, his most popular works were:
- Experimental Investigation and Pore-Scale Modeling Interpretation of Compound-Specific Transverse Dispersion in Porous Media (78 citations)
- Hydraulic Conductivity Imaging from 3-D Transient Hydraulic Tomography at Several Pumping/Observation Densities (76 citations)
- Aquifer Heterogeneity Characterization with Oscillatory Pumping: Sensitivity Analysis and Imaging Potential (76 citations)
In his most recent research, the most cited papers focused on:
- Statistics
- Thermodynamics
- Oxygen
His primary areas of study are Inverse problem, Aquifer, Algorithm, Hydraulic tomography and Applied mathematics.
His studies in Inverse problem integrate themes in fields like Uncertainty quantification, Covariance matrix, Hierarchical matrix and Inverse.
His work carried out in the field of Aquifer brings together such families of science as Hydraulic conductivity and Current.
His Algorithm study combines topics from a wide range of disciplines, such as Kalman filter, Mathematical optimization, Numerical linear algebra and Ensemble Kalman filter.
His Applied mathematics research incorporates elements of Singular value, Discrete mathematics, Krylov subspace and Square root.
His biological study spans a wide range of topics, including Mechanics and Steady state.
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