What is she best known for?
The fields of study she is best known for:
- Statistics
- Artificial intelligence
- Mechanical engineering
Her scientific interests lie mostly in Mathematical optimization, Reduction, Parametric statistics, Applied mathematics and Algorithm.
Her study in the field of Optimization problem is also linked to topics like Projection.
Her Reduction research is multidisciplinary, incorporating elements of Computer engineering, Inverse problem and Adaptation.
The various areas that Karen Willcox examines in her Parametric statistics study include Proper orthogonal decomposition, Dynamical systems theory, Aerodynamics and Adaptive sampling.
Her Algorithm research includes elements of Optimization algorithm, Vergence and Approximation theory.
Her study looks at the intersection of Nonlinear system and topics like Linear system with Decomposition, Realization, Observability and Controllability.
Her most cited work include:
- Kinetics and kinematics for translational motions in microgravity during parabolic flight. (5332 citations)
- Balanced Model Reduction via the Proper Orthogonal Decomposition (746 citations)
- A Survey of Projection-Based Model Reduction Methods for Parametric Dynamical Systems (671 citations)
What are the main themes of her work throughout her whole career to date?
Karen Willcox mostly deals with Mathematical optimization, Reduction, Applied mathematics, Algorithm and Nonlinear system.
Her Optimization problem study in the realm of Mathematical optimization interacts with subjects such as Projection.
The study incorporates disciplines such as Discretization, Control theory, Parametric statistics and Interpolation in addition to Reduction.
Her Proper orthogonal decomposition research extends to the thematically linked field of Applied mathematics.
Specifically, her work in Algorithm is concerned with the study of Computation.
Her study connects Bayesian inference and Inverse problem.
She most often published in these fields:
- Mathematical optimization (31.56%)
- Reduction (18.63%)
- Applied mathematics (12.17%)
What were the highlights of her more recent work (between 2018-2021)?
- Mathematical optimization (31.56%)
- Nonlinear system (8.37%)
- Physics based (2.28%)
In recent papers she was focusing on the following fields of study:
Her primary areas of study are Mathematical optimization, Nonlinear system, Physics based, Reduced order and Reduction.
Karen Willcox integrates Mathematical optimization and Gaussian process in her studies.
Her work carried out in the field of Nonlinear system brings together such families of science as Euler equations, Singular value decomposition, Partial differential equation, Applied mathematics and Transformation.
Her Partial differential equation research is multidisciplinary, relying on both Dynamical systems theory, Lift, Coordinate system, Estimator and Algorithm.
Her study in the field of Model order reduction is also linked to topics like Projection.
Her study in Data-driven is interdisciplinary in nature, drawing from both Nonlinear model, Inference and Control theory, Robustness.
Between 2018 and 2021, her most popular works were:
- Projection-based model reduction: Formulations for physics-based machine learning (98 citations)
- Nonlinear Model Order Reduction via Lifting Transformations and Proper Orthogonal Decomposition (54 citations)
- Lift & Learn: Physics-informed machine learning for large-scale nonlinear dynamical systems (45 citations)
In her most recent research, the most cited papers focused on:
- Statistics
- Artificial intelligence
- Mechanical engineering
Her main research concerns Nonlinear system, Reduction, Partial differential equation, Applied mathematics and Reuse.
Karen Willcox interconnects Injector, Singular value decomposition and Reduction in the investigation of issues within Nonlinear system.
Her Reduction research is multidisciplinary, incorporating perspectives in Data-driven, Nonlinear model and Control theory.
Her research in Partial differential equation intersects with topics in Dynamical systems theory, Lift, Quadratic equation, Coordinate system and Algorithm.
Her Algorithm research integrates issues from Sampling, Estimator, Variance reduction and System dynamics.
Her Applied mathematics study integrates concerns from other disciplines, such as Transformation, Class, Feature and Zero.
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