2018 - SIAM Fellow For contributions to model reduction and multifidelity methods, with applications in optimization, control, design, and uncertainty quantification of large-scale systems.
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.
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.
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.
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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Kinetics and kinematics for translational motions in microgravity during parabolic flight.
Leia Stirling;Karen Willcox;Philip Ferguson;Dava Newman.
Aviation, Space, and Environmental Medicine (2009)
A Survey of Projection-Based Model Reduction Methods for Parametric Dynamical Systems
Peter Benner;Serkan Gugercin;Karen Willcox.
Siam Review (2015)
Balanced Model Reduction via the Proper Orthogonal Decomposition
K. Willcox;J. Peraire.
AIAA Journal (2002)
Survey of Multifidelity Methods in Uncertainty Propagation, Inference, and Optimization
Benjamin Peherstorfer;Karen Willcox;Max D. Gunzburger.
Siam Review (2018)
Missing point estimation in models described by proper orthogonal decomposition
P. Astrid;S. Weiland;K. Willcox;T. Backx.
conference on decision and control (2004)
Model Reduction for Large-Scale Systems with High-Dimensional Parametric Input Space
T. Bui-Thanh;K. Willcox;O. Ghattas.
SIAM Journal on Scientific Computing (2008)
Aerodynamic Data Reconstruction and Inverse Design Using Proper Orthogonal Decomposition
Tan Bui-Thanh;Murali Damodaran;Karen E. Willcox.
AIAA Journal (2004)
Unsteady Flow Sensing and Estimation via the Gappy Proper Orthogonal Decomposition
Karen E. Willcox.
Computers & Fluids (2006)
Non-linear model reduction for uncertainty quantification in large-scale inverse problems
D. Galbally;K. Fidkowski;K. Willcox;Omar Nabih Ghattas.
International Journal for Numerical Methods in Engineering (2009)
Parameter and State Model Reduction for Large-Scale Statistical Inverse Problems
Chad Lieberman;Karen Willcox;Omar Ghattas.
SIAM Journal on Scientific Computing (2010)
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