What is he best known for?
The fields of study he is best known for:
- Quantum mechanics
- Mathematical analysis
- Statistics
Andrew J. Majda spends much of his time researching Nonlinear system, Statistical physics, Mathematical analysis, Stochastic modelling and Classical mechanics.
His Nonlinear system study also includes
- Dynamical systems theory which intersects with area such as Dynamical system,
- Attractor most often made with reference to Chaotic.
His Statistical physics study integrates concerns from other disciplines, such as Flow, Empirical orthogonal functions, Tropical convection and Barotropic fluid.
His study in the field of Hyperbolic function, Euler's formula and Euler equations also crosses realms of Hyperbolic equilibrium point.
The Stochastic modelling study combines topics in areas such as Stochastic process, Metric and Applied mathematics.
His work deals with themes such as Shock wave and Turbulence, which intersect with Classical mechanics.
His most cited work include:
- Absorbing boundary conditions for the numerical simulation of waves (2127 citations)
- Vorticity and incompressible flow (1323 citations)
- Compressible fluid flow and systems of conservation laws in several space variables (1031 citations)
What are the main themes of his work throughout his whole career to date?
His main research concerns Statistical physics, Nonlinear system, Climatology, Convection and Turbulence.
His Statistical physics research integrates issues from Scale, Dynamical systems theory, Uncertainty quantification, Data assimilation and Intermittency.
His work carried out in the field of Nonlinear system brings together such families of science as Stochastic modelling, Mathematical analysis, Classical mechanics, Applied mathematics and Gaussian.
Andrew J. Majda interconnects Vortex and Vorticity in the investigation of issues within Classical mechanics.
His studies deal with areas such as Madden–Julian oscillation and Predictability as well as Climatology.
His Convection research also works with subjects such as
- Atmospheric sciences that intertwine with fields like Kelvin wave,
- Baroclinity which intersects with area such as Barotropic fluid.
He most often published in these fields:
- Statistical physics (27.92%)
- Nonlinear system (26.88%)
- Climatology (21.47%)
What were the highlights of his more recent work (between 2014-2021)?
- Climatology (21.47%)
- Nonlinear system (26.88%)
- Statistical physics (27.92%)
In recent papers he was focusing on the following fields of study:
His scientific interests lie mostly in Climatology, Nonlinear system, Statistical physics, Madden–Julian oscillation and Convection.
The Climatology study combines topics in areas such as Atmospheric sciences and Oscillation.
The various areas that Andrew J. Majda examines in his Nonlinear system study include Dynamical systems theory, Stochastic modelling, Predictability, Errors-in-variables models and Gaussian.
His work in Statistical physics tackles topics such as Turbulence which are related to areas like Flux, Dissipation, Free energy principle and Classical mechanics.
Andrew J. Majda works mostly in the field of Madden–Julian oscillation, limiting it down to concerns involving Extratropical cyclone and, occasionally, Rossby wave.
The concepts of his Convection study are interwoven with issues in Troposphere and Mesoscale meteorology.
Between 2014 and 2021, his most popular works were:
- The MJO in a Coarse-Resolution GCM with a Stochastic Multicloud Parameterization (68 citations)
- Improving synoptic and intraseasonal variability in CFSv2 via stochastic representation of organized convection (39 citations)
- Improving synoptic and intraseasonal variability in CFSv2 via stochastic representation of organized convection (39 citations)
In his most recent research, the most cited papers focused on:
- Quantum mechanics
- Statistics
- Mathematical analysis
Andrew J. Majda spends much of his time researching Climatology, Dynamical systems theory, Statistical physics, Madden–Julian oscillation and Nonlinear system.
His study in Climatology is interdisciplinary in nature, drawing from both Convection and Atmospheric sciences.
His Dynamical systems theory research is multidisciplinary, incorporating perspectives in Phase space, Lyapunov function, Ergodicity, Applied mathematics and Complex system.
His Statistical physics research is multidisciplinary, relying on both Turbulence, Kullback–Leibler divergence and Probability density function.
His Nonlinear system research integrates issues from Statistics, Stochastic modelling, Errors-in-variables models, Gaussian and Data assimilation.
His work carried out in the field of Classical mechanics brings together such families of science as Field, Mathematical analysis and Rossby number.
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