What is he best known for?
The fields of study he is best known for:
- Quantum mechanics
- Statistics
- Thermodynamics
Omar M. Knio mostly deals with Polynomial chaos, Uncertainty quantification, Applied mathematics, FOIL method and Mathematical optimization.
His research in Polynomial chaos intersects with topics in Stochastic process and Polynomial.
His work deals with themes such as Galerkin method, Spectral method, Sensitivity analysis, Monte Carlo method and Propagation of uncertainty, which intersect with Uncertainty quantification.
His Applied mathematics study combines topics in areas such as Projection, Computational fluid dynamics, Parametric statistics, Discretization and Numerical analysis.
His FOIL method research is multidisciplinary, relying on both Combustion, Exothermic reaction, Thermodynamics and Metallurgy, Aluminium.
His Mathematical optimization research incorporates themes from Matrix decomposition and Orthogonal basis.
His most cited work include:
- Spectral Methods for Uncertainty Quantification (587 citations)
- Spectral methods for uncertainty quantification : with applications to computational fluid dynamics (505 citations)
- Uncertainty propagation using Wiener-Haar expansions (363 citations)
What are the main themes of his work throughout his whole career to date?
Mechanics, Polynomial chaos, Applied mathematics, Vortex and Mathematical optimization are his primary areas of study.
His Mechanics research incorporates elements of Combustion and Thermodynamics.
Omar M. Knio interconnects Uncertainty quantification, Sensitivity, Bayesian inference and Algorithm, Propagation of uncertainty in the investigation of issues within Polynomial chaos.
His work deals with themes such as Sensitivity analysis, Stochastic process and Parametric statistics, which intersect with Uncertainty quantification.
His Applied mathematics research integrates issues from Stochastic modelling, Galerkin method, Covariance, Covariance function and Representation.
His Vortex study frequently draws connections to adjacent fields such as Classical mechanics.
He most often published in these fields:
- Mechanics (21.76%)
- Polynomial chaos (27.00%)
- Applied mathematics (20.66%)
What were the highlights of his more recent work (between 2017-2021)?
- Applied mathematics (20.66%)
- Algorithm (9.37%)
- Polynomial chaos (27.00%)
In recent papers he was focusing on the following fields of study:
His primary scientific interests are in Applied mathematics, Algorithm, Polynomial chaos, Advection and General Circulation Model.
His Fractional calculus study in the realm of Applied mathematics interacts with subjects such as Domain decomposition methods.
His Algorithm study combines topics from a wide range of disciplines, such as Sampling, Flow, Inference and Bayesian inference.
His work is dedicated to discovering how Inference, Work are connected with Uncertainty quantification and other disciplines.
His Polynomial chaos study improves the overall literature in Monte Carlo method.
His General Circulation Model research is multidisciplinary, incorporating perspectives in Meteorology, Weather Research and Forecasting Model, Grid, Propagation of uncertainty and Trajectory planning.
Between 2017 and 2021, his most popular works were:
- TChem - A Software Toolkit for the Analysis of Complex Kinetic Models (27 citations)
- Surface air temperature variability over the Arabian Peninsula and its links to circulation patterns (24 citations)
- High-resolution assessment of solar energy resources over the Arabian Peninsula (19 citations)
In his most recent research, the most cited papers focused on:
- Quantum mechanics
- Statistics
- Thermodynamics
Omar M. Knio mainly focuses on Peninsula, Climatology, Applied mathematics, Advection and Data assimilation.
His Climatology study integrates concerns from other disciplines, such as Covariance and Eddy.
The Applied mathematics study combines topics in areas such as Sobol sequence, Representation and Stochastic modelling.
The various areas that Omar M. Knio examines in his Advection study include Exponential growth, Statistical physics, Law of total probability, Vector field and Particle number.
The concepts of his Data assimilation study are interwoven with issues in Uncertainty quantification, Work, Inference and Zeroth law of thermodynamics.
His studies examine the connections between Radiometer and genetics, as well as such issues in Meteorology, with regards to Similarity, Metric, Matrix decomposition, Grid and Downscaling.
This overview was generated by a machine learning system which analysed the scientist’s
body of work. If you have any feedback, you can
contact us
here.
