Karen Willcox
Orcid: 0000-0003-2156-9338
According to our database1,
Karen Willcox
authored at least 82 papers
between 2005 and 2024.
Collaborative distances:
Collaborative distances:
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Online presence:
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on orcid.org
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on kiwi.mit.edu
On csauthors.net:
Bibliography
2024
2023
Nonintrusive Reduced-Order Models for Parametric Partial Differential Equations via Data-Driven Operator Inference.
SIAM J. Sci. Comput., August, 2023
Predictive digital twin for optimizing patient-specific radiotherapy regimens under uncertainty in high-grade gliomas.
Frontiers Artif. Intell., February, 2023
Improving the accuracy and scalability of large-scale physics-based data-driven reduced modeling via domain decomposition.
CoRR, 2023
Multifidelity Methods for Uncertainty Quantification of a Nonlocal Model for Phase Changes in Materials.
CoRR, 2023
CoRR, 2023
Multi-output multilevel best linear unbiased estimators via semidefinite programming.
CoRR, 2023
Learning Latent Representations in High-Dimensional State Spaces Using Polynomial Manifold Constructions.
Proceedings of the 62nd IEEE Conference on Decision and Control, 2023
2022
SIAM J. Sci. Comput., 2022
Stress-constrained topology optimization of lattice-like structures using component-wise reduced order models.
CoRR, 2022
CoRR, 2022
Generalized Multifidelity Active Learning for Gaussian-process-based Reliability Analysis.
Proceedings of the Dynamic Data Driven Applications Systems - 4th International Conference, 2022
2021
Learning physics-based models from data: perspectives from inverse problems and model reduction.
Acta Numer., May, 2021
The imperative of physics-based modeling and inverse theory in computational science.
Nat. Comput. Sci., 2021
A probabilistic graphical model foundation for enabling predictive digital twins at scale.
Nat. Comput. Sci., 2021
CoRR, 2021
Non-intrusive reduced-order models for parametric partial differential equations via data-driven operator inference.
CoRR, 2021
2020
Reliab. Eng. Syst. Saf., 2020
Data-driven reduced-order models via regularized operator inference for a single-injector combustion process.
CoRR, 2020
From Physics-Based Models to Predictive Digital Twins via Interpretable Machine Learning.
CoRR, 2020
Operator inference for non-intrusive model reduction of systems with non-polynomial nonlinear terms.
CoRR, 2020
Proceedings of the Dynamic Data Driven Applications Systems, 2020
Predictive Digital Twins: Where Dynamic Data-Driven Learning Meets Physics-Based Modeling.
Proceedings of the Dynamic Data Driven Applications Systems, 2020
2019
J. Comput. Phys., 2019
Lift & Learn: Physics-informed machine learning for large-scale nonlinear dynamical systems.
CoRR, 2019
Learning physics-based reduced-order models for a single-injector combustion process.
CoRR, 2019
2018
Survey of Multifidelity Methods in Uncertainty Propagation, Inference, and Optimization.
SIAM Rev., 2018
Geometric Subspace Updates with Applications to Online Adaptive Nonlinear Model Reduction.
SIAM J. Matrix Anal. Appl., 2018
Sensitivity analysis methods for mitigating uncertainty in engineering system design.
Syst. Eng., 2018
Numerische Mathematik, 2018
SIAM/ASA J. Uncertain. Quantification, 2018
Multifidelity Preconditioning of the Cross-Entropy Method for Rare Event Simulation and Failure Probability Estimation.
SIAM/ASA J. Uncertain. Quantification, 2018
SIAM/ASA J. Uncertain. Quantification, 2018
Nonlinear Model Order Reduction via Lifting Transformations and Proper Orthogonal Decomposition.
CoRR, 2018
Proceedings of the Advances in Neural Information Processing Systems 31: Annual Conference on Neural Information Processing Systems 2018, 2018
2017
SIAM J. Sci. Comput., 2017
SIAM J. Sci. Comput., 2017
SIAM J. Sci. Comput., 2017
Feedback Control for Systems with Uncertain Parameters Using Online-Adaptive Reduced Models.
SIAM J. Appl. Dyn. Syst., 2017
Optimal L<sub>2</sub>-norm empirical importance weights for the change of probability measure.
Stat. Comput., 2017
Combining multiple surrogate models to accelerate failure probability estimation with expensive high-fidelity models.
J. Comput. Phys., 2017
A decomposition-based uncertainty quantification approach for environmental impacts of aviation technology and operation.
Artif. Intell. Eng. Des. Anal. Manuf., 2017
Proceedings of the Advances in Neural Information Processing Systems 30: Annual Conference on Neural Information Processing Systems 2017, 2017
2016
SIAM J. Sci. Comput., 2016
Scalable posterior approximations for large-scale Bayesian inverse problems via likelihood-informed parameter and state reduction.
J. Comput. Phys., 2016
Adv. Model. Simul. Eng. Sci., 2016
Bayesian Optimization with a Finite Budget: An Approximate Dynamic Programming Approach.
Proceedings of the Advances in Neural Information Processing Systems 29: Annual Conference on Neural Information Processing Systems 2016, 2016
2015
SIAM J. Sci. Comput., 2015
A Survey of Projection-Based Model Reduction Methods for Parametric Dynamical Systems.
SIAM Rev., 2015
Detecting and Adapting to Parameter Changes for Reduced Models of Dynamic Data-driven Application Systems.
Proceedings of the International Conference on Computational Science, 2015
2014
Nonlinear Goal-Oriented Bayesian Inference: Application to Carbon Capture and Storage.
SIAM J. Sci. Comput., 2014
Reliab. Eng. Syst. Saf., 2014
Proceedings of the International Conference on Computational Science, 2014
2013
Proceedings of the International Conference on Computational Science, 2013
2012
Goal-Oriented Inference: Approach, Linear Theory, and Application to Advection Diffusion.
SIAM J. Sci. Comput., 2012
Reliab. Eng. Syst. Saf., 2012
Proceedings of the International Conference on Computational Science, 2012
Proceedings of the 15th International Conference on Information Fusion, 2012
2010
SIAM J. Sci. Comput., 2010
2008
IEEE Trans. Autom. Control., 2008
Model Reduction for Large-Scale Systems with High-Dimensional Parametric Input Space.
SIAM J. Sci. Comput., 2008
2007
J. Comput. Phys., 2007
Proceedings of the Computational Science, 2007
Proceedings of the 46th IEEE Conference on Decision and Control, 2007
2006
MPC for Large-Scale Systems via Model Reduction and Multiparametric Quadratic Programming.
Proceedings of the 45th IEEE Conference on Decision and Control, 2006
2005
Fourier Series for Accurate, Stable, Reduced-Order Models in Large-Scale Linear Applications.
SIAM J. Sci. Comput., 2005
An Optimization Frame work for Goal-Oriented, Model-Based Reduction of Large-Scale Systems.
Proceedings of the 44th IEEE IEEE Conference on Decision and Control and 8th European Control Conference Control, 2005