World's Best Scientists 2026 revealed!

D-Index & Metrics

Engineering and Technology

D-Index
65
Citations
25119
World Ranking
1485
National Ranking
485

Research.com Recognitions

  • 2018 - SIAM Fellow For contributions to model reduction and multifidelity methods, with applications in optimization, control, design, and uncertainty quantification of large-scale systems.

Overview

Karen Willcox is affiliated with The University of Texas at Austin in the United States. Their research primarily spans the fields of engineering and physics and astronomy, with a focus on statistical and nonlinear physics, statistics, probability and uncertainty, aerospace engineering, control and systems engineering, and computational theory and mathematics.

Their work covers a variety of topics including model reduction and neural networks, probabilistic and robust engineering design, advanced multi-objective optimization algorithms, nuclear engineering thermal-hydraulics, manufacturing process and optimization, numerical methods for differential equations, and fluid dynamics and turbulent flows.

Among recent publications, the following papers illustrate the scope of their research:

  • Lift & Learn: Physics-informed machine learning for large-scale nonlinear dynamical systems, 2020, Physica D Nonlinear Phenomena
  • Scaling digital twins from the artisanal to the industrial, 2021, Nature Computational Science
  • Data-driven physics-based digital twins via a library of component-based reduced-order models, 2020, International Journal for Numerical Methods in Engineering
  • Learning physics-based models from data: perspectives from inverse problems and model reduction, 2021, Acta Numerica
  • Operator inference for non-intrusive model reduction of systems with non-polynomial nonlinear terms, 2020, Computer Methods in Applied Mechanics and Engineering

Karen Willcox frequently publishes in venues such as arXiv (Cornell University), Computer Methods in Applied Mechanics and Engineering, Nature Computational Science, AIAA SCITECH 2023 Forum, and AIAA Journal.

Frequent collaborators include Anirban Chaudhuri, Michael G. Kapteyn, Boris Krämer, Shane A. McQuarrie, and Omar Ghattas.

In 2018, Willcox was named a SIAM Fellow for contributions to model reduction and multifidelity methods, particularly related to optimization, control, design, and uncertainty quantification of large-scale systems.

Best Publications

  • Kinetics and kinematics for translational motions in microgravity during parabolic flight.

    Leia Stirling;Karen Willcox;Philip Ferguson;Dava Newman

  • A Survey of Projection-Based Model Reduction Methods for Parametric Dynamical Systems

    Peter Benner;Serkan Gugercin;Karen Willcox

  • Balanced Model Reduction via the Proper Orthogonal Decomposition

    K. Willcox;J. Peraire

  • Survey of Multifidelity Methods in Uncertainty Propagation, Inference, and Optimization

    Benjamin Peherstorfer;Karen Willcox;Max D. Gunzburger

  • Missing point estimation in models described by proper orthogonal decomposition

    P. Astrid;S. Weiland;K. Willcox;T. Backx

  • Aerodynamic Data Reconstruction and Inverse Design Using Proper Orthogonal Decomposition

    Tan Bui-Thanh;Murali Damodaran;Karen E. Willcox

  • Unsteady Flow Sensing and Estimation via the Gappy Proper Orthogonal Decomposition

    Karen E. Willcox

  • Model Reduction for Large-Scale Systems with High-Dimensional Parametric Input Space

    T. Bui-Thanh;K. Willcox;O. Ghattas

  • Data-driven operator inference for nonintrusive projection-based model reduction

    Benjamin Peherstorfer;Karen E Willcox

  • Projection-based model reduction: Formulations for physics-based machine learning

    Renee Swischuk;Laura Mainini;Benjamin Peherstorfer;Karen Willcox

  • Non-linear model reduction for uncertainty quantification in large-scale inverse problems

    D. Galbally;K. Fidkowski;K. Willcox;Omar Nabih Ghattas

  • Model Reduction and Approximation: Theory and Algorithms

    Peter Benner;Albert Cohen;Mario Ohlberger;Karen Willcox

  • Parameter and State Model Reduction for Large-Scale Statistical Inverse Problems

    Chad Lieberman;Karen Willcox;Omar Ghattas

  • Optimal Model Management for Multifidelity Monte Carlo Estimation

    Benjamin Peherstorfer;Karen Willcox;Max D. Gunzburger

  • Proper orthogonal decomposition extensions for parametric applications in compressible aerodynamics

    T. Bui-Thanh;Murali Damodaran;Karen Willcox

  • Localized Discrete Empirical Interpolation Method

    Benjamin Peherstorfer;Daniel Butnaru;Karen E. Willcox;Hans-Joachim Bungartz

  • Surrogate-Based Optimization Using Multifidelity Models with Variable Parameterization and Corrected Space Mapping

    Theresa Robinson;M.S. Eldred;K.E. Willcox;R. Haimes

  • Workshop Report on Basic Research Needs for Scientific Machine Learning: Core Technologies for Artificial Intelligence

    Nathan Baker;Frank Alexander;Timo Bremer;Aric Hagberg

  • Lift & Learn: Physics-informed machine learning for large-scale nonlinear dynamical systems

    Elizabeth Qian;Boris Kramer;Benjamin Peherstorfer;Karen Willcox

  • Goal-oriented, model-constrained optimization for reduction of large-scale systems

    T. Bui-Thanh;K. Willcox;O. Ghattas;B. van Bloemen Waanders

Frequent Co-Authors

Omar Ghattas
Omar Ghattas The University of Texas at Austin
Max D. Gunzburger
Max D. Gunzburger Florida State University
Peter Benner
Peter Benner Max Planck Institute for Dynamics of Complex Technical Systems
George Biros
George Biros The University of Texas at Austin
Ilan Kroo
Ilan Kroo Stanford University
Serkan Gugercin
Serkan Gugercin Virginia Tech

If you think any of the details on this page are incorrect, let us know.

Report an issue

We appreciate your kind effort to assist us to improve this page, it would be helpful providing us with as much detail as possible in the text box below:

Related Online Degrees & Career Pathways

Exploring Engineering and Technology in the USA can open doors to numerous advanced career paths. Many students and professionals are now looking to boost their credentials with flexible, online degree programs. For those interested in organizational strategy and leadership roles, the best organizational leadership master's programs can help develop crucial management skills needed in technical industries.

Operations management is another vital area. The best online mba in operations management programs offer specialized training in efficient business processes—skills highly valued in engineering-focused companies.

If you are considering an MBA, some schools offer flexibility by waiving traditional test requirements. Explore options for an mba online no gmat to streamline your application process and start advancing your career sooner.

Cost is also a critical factor for many students. Fortunately, there are high-quality programs designed with affordability in mind, including an online mba under $30k. These opportunities allow you to pursue respected business and management degrees without overwhelming financial pressure.

Best Scientists Citing Karen Willcox

Trending Scientists

Recently Published Articles