What is she best known for?
The fields of study she is best known for:
- Statistics
- Mathematical analysis
- Algorithm
Nancy Nichols focuses on Control theory, Applied mathematics, Data assimilation, Controllability and Observability.
Her study in the field of Descriptor systems and Full state feedback also crosses realms of Polynomial matrix and Quadratic function.
Her work deals with themes such as Transformation, Coordinate-free, Shallow water equations and Linear system, which intersect with Applied mathematics.
The study incorporates disciplines such as Algorithm, Statistics, Thermal and Interpolation in addition to Data assimilation.
Her Controllability research incorporates themes from Derivative, Equivalence, Canonical form and Output feedback.
Her work focuses on many connections between State and other disciplines, such as Measure, that overlap with her field of interest in Matrix.
Her most cited work include:
- Robust pole assignment in linear state feedback (905 citations)
- Are patterns of growth adaptive (138 citations)
- Approximate Gauss-Newton Methods for Nonlinear Least Squares Problems (123 citations)
What are the main themes of her work throughout her whole career to date?
Nancy Nichols spends much of her time researching Data assimilation, Control theory, Applied mathematics, Meteorology and Mathematical optimization.
Her work carried out in the field of Data assimilation brings together such families of science as Covariance matrix, Covariance, Statistics, Errors-in-variables models and Numerical weather prediction.
Her study involves Descriptor systems, Output feedback, Optimal control, Robustness and Linear system, a branch of Control theory.
Nancy Nichols focuses mostly in the field of Applied mathematics, narrowing it down to matters related to Condition number and, in some cases, Hessian matrix.
The various areas that Nancy Nichols examines in her Meteorology study include Climatology and Predictability.
In her research, Control theory is intimately related to Nonlinear system, which falls under the overarching field of Mathematical optimization.
She most often published in these fields:
- Data assimilation (37.89%)
- Control theory (24.67%)
- Applied mathematics (21.59%)
What were the highlights of her more recent work (between 2014-2021)?
- Data assimilation (37.89%)
- Meteorology (14.10%)
- Statistics (8.81%)
In recent papers she was focusing on the following fields of study:
Nancy Nichols mainly focuses on Data assimilation, Meteorology, Statistics, Covariance and Numerical weather prediction.
Nancy Nichols has included themes like Sampling, Covariance matrix, Satellite, Applied mathematics and Remote sensing in her Data assimilation study.
Her Applied mathematics study incorporates themes from Linear map, Attractor, Econometrics and Bayesian probability.
The Statistics study combines topics in areas such as Flood myth and Flood forecasting.
Her Covariance research includes elements of Condition number and Matrix.
Her Matrix study integrates concerns from other disciplines, such as Cloud top, Errors-in-variables models, Representation and Regression.
Between 2014 and 2021, her most popular works were:
- On the representation error in data assimilation (101 citations)
- Diagnosing Observation Error Correlations for Doppler Radar Radial Winds in the Met Office UKV Model Using Observation-Minus-Background and Observation-Minus-Analysis Statistics (56 citations)
- Theoretical insight into diagnosing observation error correlations using observation-minus-background and observation-minus-analysis statistics (55 citations)
In her most recent research, the most cited papers focused on:
- Statistics
- Mathematical analysis
- Geometry
Her primary scientific interests are in Data assimilation, Statistics, Covariance matrix, Numerical weather prediction and Meteorology.
Her studies deal with areas such as Matrix, Covariance, Eigenvalues and eigenvectors and Forecast skill as well as Data assimilation.
Her Matrix study frequently draws parallels with other fields, such as Applied mathematics.
Her research in Applied mathematics intersects with topics in Condition number, Linear map, Hessian matrix and Bayesian probability.
The concepts of her Numerical weather prediction study are interwoven with issues in Wind speed, Ranging, Geodesy and Radiosonde.
Her Meteorology research integrates issues from Doppler radar and Atmospheric sciences.
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.
