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Karen E. Willcox
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2020 – today
- 2024
[j47]Karen Willcox
, Brittany Segundo:
The role of computational science in digital twins. Nat. Comput. Sci. 4(3): 147-149 (2024)- [j46]Alberto Ferrari, Karen Willcox:
Digital twins in mechanical and aerospace engineering. Nat. Comput. Sci. 4(3): 178-183 (2024) - [i31]Ionut-Gabriel Farcas, Rayomand P. Gundevia, Ramakanth Munipalli, Karen E. Willcox:
Distributed computing for physics-based data-driven reduced modeling at scale: Application to a rotating detonation rocket engine. CoRR abs/2407.09994 (2024) - [i30]Marco Tezzele, Steven Carr, Ufuk Topcu, Karen E. Willcox:
Adaptive planning for risk-aware predictive digital twins. CoRR abs/2407.20490 (2024) - [i29]Shane A. McQuarrie, Anirban Chaudhuri, Karen E. Willcox, Mengwu Guo:
Bayesian learning with Gaussian processes for low-dimensional representations of time-dependent nonlinear systems. CoRR abs/2408.03455 (2024) - [i28]Julie Pham, Omar Ghattas, Noel Clemens, Karen Willcox:
Real-time aerodynamic load estimation for hypersonics via strain-based inverse maps. CoRR abs/2408.15286 (2024) - 2023
- [j45]Anirban Chaudhuri, Graham Pash, David A. Hormuth, Guillermo Lorenzo, Michael G. Kapteyn, Chengyue Wu, Ernesto A. B. F. Lima, Thomas E. Yankeelov, Karen Willcox:
Predictive digital twin for optimizing patient-specific radiotherapy regimens under uncertainty in high-grade gliomas. Frontiers Artif. Intell. 6 (2023) - [j44]Shane A. McQuarrie, Parisa Khodabakhshi, Karen E. Willcox:
Nonintrusive Reduced-Order Models for Parametric Partial Differential Equations via Data-Driven Operator Inference. SIAM J. Sci. Comput. 45(4): 1917- (2023) - [c17]Rudy Geelen, Laura Balzano, Karen Willcox:
Learning Latent Representations in High-Dimensional State Spaces Using Polynomial Manifold Constructions. CDC 2023: 4960-4965 - [i27]Matteo Croci, Karen E. Willcox, Stephen J. Wright:
Multi-output multilevel best linear unbiased estimators via semidefinite programming. CoRR abs/2301.07831 (2023) - [i26]Rudy Geelen, Laura Balzano, Karen Willcox:
Learning latent representations in high-dimensional state spaces using polynomial manifold constructions. CoRR abs/2306.13748 (2023) - [i25]Matteo Torzoni, Marco Tezzele, Stefano Mariani, Andrea Manzoni, Karen E. Willcox:
A digital twin framework for civil engineering structures. CoRR abs/2308.01445 (2023) - [i24]Rudy Geelen, Laura Balzano, Stephen Wright, Karen Willcox:
Learning physics-based reduced-order models from data using nonlinear manifolds. CoRR abs/2308.02802 (2023) - [i23]Anirban Chaudhuri, Graham Pash, David A. Hormuth II, Guillermo Lorenzo, Michael G. Kapteyn, Chengyue Wu, Ernesto A. B. F. Lima, Thomas E. Yankeelov, Karen Willcox:
Predictive Digital Twin for Optimizing Patient-Specific Radiotherapy Regimens under Uncertainty in High-Grade Gliomas. CoRR abs/2308.12429 (2023) - [i22]Parisa Khodabakhshi, Olena Burkovska, Karen Willcox, Max D. Gunzburger:
Multifidelity Methods for Uncertainty Quantification of a Nonlocal Model for Phase Changes in Materials. CoRR abs/2310.10750 (2023) - [i21]Ionut-Gabriel Farcas, Rayomand P. Gundevia, Ramakanth Munipalli, Karen E. Willcox:
Improving the accuracy and scalability of large-scale physics-based data-driven reduced modeling via domain decomposition. CoRR abs/2311.00883 (2023) - 2022
- [j43]Elizabeth Qian, Ionut-Gabriel Farcas, Karen Willcox:
Reduced Operator Inference for Nonlinear Partial Differential Equations. SIAM J. Sci. Comput. 44(4): 1934- (2022) - [c16]Anirban Chaudhuri, Karen Willcox:
Generalized Multifidelity Active Learning for Gaussian-process-based Reliability Analysis. DDDAS 2022: 17-26 - [i20]Mengwu Guo, Shane A. McQuarrie, Karen E. Willcox:
Bayesian operator inference for data-driven reduced-order modeling. CoRR abs/2204.10829 (2022) - [i19]Rudy Geelen, Stephen Wright, Karen Willcox:
Operator inference for non-intrusive model reduction with nonlinear manifolds. CoRR abs/2205.02304 (2022) - [i18]Sean McBane, Youngsoo Choi, Karen Willcox:
Stress-constrained topology optimization of lattice-like structures using component-wise reduced order models. CoRR abs/2205.09629 (2022) - 2021
- [j42]Omar Ghattas, Karen Willcox:
Learning physics-based models from data: perspectives from inverse problems and model reduction. Acta Numer. 30: 445-554 (2021) - [j41]Parisa Khodabakhshi, Karen E. Willcox, Max D. Gunzburger:
A multifidelity method for a nonlocal diffusion model. Appl. Math. Lett. 121: 107361 (2021) - [j40]Karen E. Willcox, Omar Ghattas, Patrick Heimbach:
The imperative of physics-based modeling and inverse theory in computational science. Nat. Comput. Sci. 1(3): 166-168 (2021) - [j39]Steven A. Niederer, Michael S. Sacks, Mark Girolami, Karen Willcox:
Scaling digital twins from the artisanal to the industrial. Nat. Comput. Sci. 1(5): 313-320 (2021) - [j38]Michael G. Kapteyn, Jacob V. R. Pretorius, Karen E. Willcox:
A probabilistic graphical model foundation for enabling predictive digital twins at scale. Nat. Comput. Sci. 1(5): 337-347 (2021) - [i17]Anirban Chaudhuri, Boris Kramer, Matthew Norton, Johannes O. Royset, Karen Willcox:
Certifiable Risk-Based Engineering Design Optimization. CoRR abs/2101.05129 (2021) - [i16]Elizabeth Qian, Ionut-Gabriel Farcas, Karen Willcox:
Reduced operator inference for nonlinear partial differential equations. CoRR abs/2102.00083 (2021) - [i15]Shane A. McQuarrie, Parisa Khodabakhshi, Karen E. Willcox:
Non-intrusive reduced-order models for parametric partial differential equations via data-driven operator inference. CoRR abs/2110.07653 (2021) - [i14]Thomas O'Leary-Roseberry, Xiaosong Du, Anirban Chaudhuri, Joaquim R. R. A. Martins, Karen Willcox, Omar Ghattas:
Adaptive Projected Residual Networks for Learning Parametric Maps from Sparse Data. CoRR abs/2112.07096 (2021) - 2020
- [j37]Anirban Chaudhuri, Boris Kramer, Karen E. Willcox:
Information Reuse for Importance Sampling in Reliability-Based Design Optimization. Reliab. Eng. Syst. Saf. 201: 106853 (2020) - [j36]Rémi R. Lam, Olivier Zahm, Youssef M. Marzouk, Karen E. Willcox:
Multifidelity Dimension Reduction via Active Subspaces. SIAM J. Sci. Comput. 42(2): A929-A956 (2020) - [c15]Michael G. Kapteyn, Karen E. Willcox:
Predictive Digital Twins: Where Dynamic Data-Driven Learning Meets Physics-Based Modeling. DDDAS 2020: 3-7 - [c14]Stefanie J. Salinger, Michael G. Kapteyn, Cory Kays, Jacob V. R. Pretorius, Karen E. Willcox:
A Hardware Testbed for Dynamic Data-Driven Aerospace Digital Twins. DDDAS 2020: 37-45 - [i13]Peter Benner, Pawan Goyal, Boris Kramer, Benjamin Peherstorfer, Karen Willcox:
Operator inference for non-intrusive model reduction of systems with non-polynomial nonlinear terms. CoRR abs/2002.09726 (2020) - [i12]Michael G. Kapteyn, Karen E. Willcox:
From Physics-Based Models to Predictive Digital Twins via Interpretable Machine Learning. CoRR abs/2004.11356 (2020) - [i11]Shane A. McQuarrie, Cheng Huang, Karen Willcox:
Data-driven reduced-order models via regularized operator inference for a single-injector combustion process. CoRR abs/2008.02862 (2020) - [i10]Michael G. Kapteyn, Jacob V. R. Pretorius, Karen E. Willcox:
A Probabilistic Graphical Model Foundation for Enabling Predictive Digital Twins at Scale. CoRR abs/2012.05841 (2020)
2010 – 2019
- 2019
- [j35]Boris Kramer, Alexandre Noll Marques, Benjamin Peherstorfer, Umberto Villa, Karen Willcox:
Multifidelity probability estimation via fusion of estimators. J. Comput. Phys. 392: 385-402 (2019) - [i9]Boris Kramer, Alexandre Noll Marques, Benjamin Peherstorfer, Umberto Villa, Karen Willcox:
Multifidelity probability estimation via fusion of estimators. CoRR abs/1905.02679 (2019) - [i8]Boris Kramer, Karen E. Willcox:
Balanced Truncation Model Reduction for Lifted Nonlinear Systems. CoRR abs/1907.12084 (2019) - [i7]Renee Swischuk, Boris Kramer, Cheng Huang, Karen Willcox:
Learning physics-based reduced-order models for a single-injector combustion process. CoRR abs/1908.03620 (2019) - [i6]Anirban Chaudhuri, Alexandre Noll Marques, Karen E. Willcox:
mfEGRA: Multifidelity Efficient Global Reliability Analysis. CoRR abs/1910.02497 (2019) - [i5]Elizabeth Qian, Boris Kramer, Benjamin Peherstorfer, Karen Willcox:
Lift & Learn: Physics-informed machine learning for large-scale nonlinear dynamical systems. CoRR abs/1912.08177 (2019) - 2018
- [j34]Elizabeth Qian, Benjamin Peherstorfer, Daniel O'Malley, Velimir V. Vesselinov, Karen Willcox:
Multifidelity Monte Carlo Estimation of Variance and Sensitivity Indices. SIAM/ASA J. Uncertain. Quantification 6(2): 683-706 (2018) - [j33]Benjamin Peherstorfer, Boris Kramer, Karen Willcox:
Multifidelity Preconditioning of the Cross-Entropy Method for Rare Event Simulation and Failure Probability Estimation. SIAM/ASA J. Uncertain. Quantification 6(2): 737-761 (2018) - [j32]Matthias Heinkenschloss, Boris Kramer, Timur Takhtaganov, Karen Willcox:
Conditional-Value-at-Risk Estimation via Reduced-Order Models. SIAM/ASA J. Uncertain. Quantification 6(4): 1395-1423 (2018) - [j31]Benjamin Peherstorfer, Max D. Gunzburger, Karen Willcox:
Convergence analysis of multifidelity Monte Carlo estimation. Numerische Mathematik 139(3): 683-707 (2018) - [j30]Qinxian Chelsea Curran, Douglas L. Allaire, Karen E. Willcox:
Sensitivity analysis methods for mitigating uncertainty in engineering system design. Syst. Eng. 21(3): 191-209 (2018) - [j29]Ralf Zimmermann, Benjamin Peherstorfer, Karen Willcox:
Geometric Subspace Updates with Applications to Online Adaptive Nonlinear Model Reduction. SIAM J. Matrix Anal. Appl. 39(1): 234-261 (2018) - [j28]Benjamin Peherstorfer, Karen Willcox, Max D. Gunzburger:
Survey of Multifidelity Methods in Uncertainty Propagation, Inference, and Optimization. SIAM Rev. 60(3): 550-591 (2018) - [j27]Ulrich Rüde, Karen Willcox, Lois Curfman McInnes, Hans De Sterck:
Research and Education in Computational Science and Engineering. SIAM Rev. 60(3): 707-754 (2018) - [c13]Alexandre Noll Marques, Rémi Lam, Karen Willcox:
Contour location via entropy reduction leveraging multiple information sources. NeurIPS 2018: 5223-5233 - [i4]Alexandre Noll Marques, Rémi R. Lam, Karen E. Willcox:
Contour location via entropy reduction leveraging multiple information sources. CoRR abs/1805.07489 (2018) - [i3]Benjamin Peherstorfer, Karen Willcox, Max D. Gunzburger:
Survey of multifidelity methods in uncertainty propagation, inference, and optimization. CoRR abs/1806.10761 (2018) - [i2]Boris Kramer, Karen Willcox:
Nonlinear Model Order Reduction via Lifting Transformations and Proper Orthogonal Decomposition. CoRR abs/1808.02086 (2018) - 2017
- [j26]Sergio Amaral, Douglas L. Allaire, Elena De La Rosa Blanco, Karen Willcox:
A decomposition-based uncertainty quantification approach for environmental impacts of aviation technology and operation. Artif. Intell. Eng. Des. Anal. Manuf. 31(3): 251-264 (2017) - [j25]Benjamin Peherstorfer, Boris Kramer, Karen Willcox:
Combining multiple surrogate models to accelerate failure probability estimation with expensive high-fidelity models. J. Comput. Phys. 341: 61-75 (2017) - [j24]Sergio Amaral, Douglas L. Allaire, Karen Willcox:
Optimal L2-norm empirical importance weights for the change of probability measure. Stat. Comput. 27(3): 625-643 (2017) - [j23]Boris Kramer, Benjamin Peherstorfer, Karen Willcox:
Feedback Control for Systems with Uncertain Parameters Using Online-Adaptive Reduced Models. SIAM J. Appl. Dyn. Syst. 16(3): 1563-1586 (2017) - [j22]Benjamin Peherstorfer, Serkan Gugercin, Karen Willcox:
Data-Driven Reduced Model Construction with Time-Domain Loewner Models. SIAM J. Sci. Comput. 39(5) (2017) - [j21]Elizabeth Qian, Martin A. Grepl, Karen Veroy, Karen Willcox:
A Certified Trust Region Reduced Basis Approach to PDE-Constrained Optimization. SIAM J. Sci. Comput. 39(5) (2017) - [j20]Alessio Spantini, Tiangang Cui, Karen Willcox, Luis Tenorio, Youssef M. Marzouk:
Goal-Oriented Optimal Approximations of Bayesian Linear Inverse Problems. SIAM J. Sci. Comput. 39(5) (2017) - [c12]Rémi Lam, Karen Willcox:
Lookahead Bayesian Optimization with Inequality Constraints. NIPS 2017: 1890-1900 - 2016
- [j19]Benjamin Peherstorfer, Karen Willcox:
Dynamic data-driven model reduction: adapting reduced models from incomplete data. Adv. Model. Simul. Eng. Sci. 3(1): 11:1-11:22 (2016) - [j18]Tiangang Cui, Youssef M. Marzouk, Karen Willcox:
Scalable posterior approximations for large-scale Bayesian inverse problems via likelihood-informed parameter and state reduction. J. Comput. Phys. 315: 363-387 (2016) - [j17]Benjamin Peherstorfer, Karen Willcox, Max D. Gunzburger:
Optimal Model Management for Multifidelity Monte Carlo Estimation. SIAM J. Sci. Comput. 38(5) (2016) - [j16]Ralf Zimmermann, Karen Willcox:
An Accelerated Greedy Missing Point Estimation Procedure. SIAM J. Sci. Comput. 38(5) (2016) - [c11]Rémi Lam, Karen Willcox, David H. Wolpert:
Bayesian Optimization with a Finite Budget: An Approximate Dynamic Programming Approach. NIPS 2016: 883-891 - [i1]Ulrich Rüde, Karen Willcox, Lois Curfman McInnes, Hans De Sterck, George Biros, Hans-Joachim Bungartz, James Corones, Evin Cramer, James Crowley, Omar Ghattas, Max D. Gunzburger, Michael Hanke, Robert J. Harrison, Michael A. Heroux, Jan S. Hesthaven, Peter K. Jimack, Chris Johnson, Kirk E. Jordan, David E. Keyes, Rolf H. Krause, Vipin Kumar, Stefan Mayer, Juan Meza, Knut Martin Mørken, J. Tinsley Oden, Linda R. Petzold, Padma Raghavan, Suzanne M. Shontz, Anne E. Trefethen, Peter R. Turner, Vladimir V. Voevodin, Barbara I. Wohlmuth, Carol S. Woodward:
Research and Education in Computational Science and Engineering. CoRR abs/1610.02608 (2016) - 2015
- [j15]Peter Benner, Serkan Gugercin, Karen Willcox:
A Survey of Projection-Based Model Reduction Methods for Parametric Dynamical Systems. SIAM Rev. 57(4): 483-531 (2015) - [j14]Qifeng Liao, Karen Willcox:
A Domain Decomposition Approach for Uncertainty Analysis. SIAM J. Sci. Comput. 37(1) (2015) - [j13]Benjamin Peherstorfer, Karen Willcox:
Online Adaptive Model Reduction for Nonlinear Systems via Low-Rank Updates. SIAM J. Sci. Comput. 37(4) (2015) - [c10]Benjamin Peherstorfer, Karen Willcox:
Detecting and Adapting to Parameter Changes for Reduced Models of Dynamic Data-driven Application Systems. ICCS 2015: 2553-2562 - 2014
- [j12]Douglas L. Allaire, George Noel, Karen Willcox, Rebecca Cointin:
Uncertainty quantification of an Aviation Environmental Toolsuite. Reliab. Eng. Syst. Saf. 126: 14-24 (2014) - [j11]Benjamin Peherstorfer, Daniel Butnaru, Karen Willcox, Hans-Joachim Bungartz:
Localized Discrete Empirical Interpolation Method. SIAM J. Sci. Comput. 36(1) (2014) - [j10]Chad Lieberman, Karen Willcox:
Nonlinear Goal-Oriented Bayesian Inference: Application to Carbon Capture and Storage. SIAM J. Sci. Comput. 36(3) (2014) - [c9]Douglas L. Allaire, D. Kordonowy, M. Lecerf, Laura Mainini, Karen Willcox:
Multifidelity DDDAS Methods with Application to a Self-aware Aerospace Vehicle. ICCS 2014: 1182-1192 - 2013
- [j9]Chad Lieberman, Karen Willcox:
Goal-Oriented Inference: Approach, Linear Theory, and Application to Advection Diffusion. SIAM Rev. 55(3): 493-519 (2013) - [c8]Douglas L. Allaire, J. Chambers, Raghvendra V. Cowlagi, David N. Kordonowy, M. Lecerf, Laura Mainini, F. Ulker, Karen Willcox:
An Offline/Online DDDAS Capability for Self-Aware Aerospace Vehicles. ICCS 2013: 1959-1968 - 2012
- [j8]Douglas L. Allaire, Karen Willcox:
A variance-based sensitivity index function for factor prioritization. Reliab. Eng. Syst. Saf. 107: 107-114 (2012) - [j7]Chad Lieberman, Karen Willcox:
Goal-Oriented Inference: Approach, Linear Theory, and Application to Advection Diffusion. SIAM J. Sci. Comput. 34(4) (2012) - [c7]Douglas L. Allaire, Karen Willcox:
Fusing information from multifidelity computer models of physical systems. FUSION 2012: 2458-2465 - [c6]Douglas L. Allaire, George Biros, J. Chambers, Omar Ghattas, D. Kordonowy, Karen Willcox:
Dynamic Data Driven Methods for Self-aware Aerospace Vehicles. ICCS 2012: 1206-1210 - 2010
- [j6]Chad Lieberman, Karen Willcox, Omar Ghattas:
Parameter and State Model Reduction for Large-Scale Statistical Inverse Problems. SIAM J. Sci. Comput. 32(5): 2523-2542 (2010)
2000 – 2009
- 2008
- [j5]Serkan Gugercin, Karen Willcox:
Krylov projection framework for Fourier model reduction. Autom. 44(1): 209-215 (2008) - [j4]Tan Bui-Thanh, Karen Willcox, Omar Ghattas:
Model Reduction for Large-Scale Systems with High-Dimensional Parametric Input Space. SIAM J. Sci. Comput. 30(6): 3270-3288 (2008) - [j3]Patricia Astrid, Siep Weiland, Karen Willcox, Ton Backx:
Missing Point Estimation in Models Described by Proper Orthogonal Decomposition. IEEE Trans. Autom. Control. 53(10): 2237-2251 (2008) - 2007
- [j2]Tan Bui-Thanh, Karen Willcox, Omar Ghattas, Bart G. van Bloemen Waanders:
Goal-oriented, model-constrained optimization for reduction of large-scale systems. J. Comput. Phys. 224(2): 880-896 (2007) - [c5]Leia A. Stirling, Alessandro Arsie, Emilio Frazzoli, Karen Willcox, Dava Newman:
Application of quantized control to human self-rotation maneuvers in microgravity. CDC 2007: 3907-3912 - [c4]Omar Bashir, Omar Ghattas, Judith Hill, Bart G. van Bloemen Waanders, Karen Willcox:
Hessian-Based Model Reduction for Large-Scale Data Assimilation Problems. International Conference on Computational Science (1) 2007: 1010-1017 - 2006
- [c3]Svein Hovland, Karen Willcox, Jan Tommy Gravdahl:
MPC for Large-Scale Systems via Model Reduction and Multiparametric Quadratic Programming. CDC 2006: 3418-3423 - 2005
- [j1]Karen Willcox, Alexandre Megretski:
Fourier Series for Accurate, Stable, Reduced-Order Models in Large-Scale Linear Applications. SIAM J. Sci. Comput. 26(3): 944-962 (2005) - [c2]Karen Willcox, Omar Ghattas, Bart G. van Bloemen Waanders, Brett W. Bader:
An Optimization Frame work for Goal-Oriented, Model-Based Reduction of Large-Scale Systems. CDC/ECC 2005: 2265-2271 - 2004
- [c1]Patricia Astrid, Siep Weiland, Karen Willcox, Ton Backx:
Missing point estimation in models described by proper orthogonal decomposition. CDC 2004: 1767-1772
Coauthor Index
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