Karen Willcox

University of Texas at Austin

AlgorithmMathematical optimizationEngineeringAerodynamicsInverse problemOptimization problemNonlinear systemComputational fluid dynamicsMonte Carlo methodSystems engineeringMultidisciplinary approachInferenceApplied mathematicsUncertainty quantificationMathematicsEngineering design processComputer scienceControl theoryPartial differential equationReduction (complexity)

280Publications

48H-index

13.3kCitations

Publications 250

Newest

#1Parisa Khodabakhshi (University of Texas at Austin)H-Index: 5

#2Karen WillcoxH-Index: 48

Last. Max D. GunzburgerH-Index: 74

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Abstract Nonlocal models feature a finite length scale, referred to as the horizon, such that points separated by a distance smaller than the horizon interact with each other. Such models have proven to be useful in a variety of settings. However, due to the reduced sparsity of discretizations, they are also generally computationally more expensive compared to their local differential equation counterparts. We introduce a multifidelity Monte Carlo method that combines the high-fidelity nonlocal ...

Non-intrusive reduced-order models for parametric partial differential equations via data-driven operator inference

#1Shane A. McQuarrie (University of Texas at Austin)H-Index: 4

#2Parisa Khodabakhshi (University of Texas at Austin)H-Index: 5

Last. Karen Willcox (University of Texas at Austin)H-Index: 48

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This work formulates a new approach to reduced modeling of parameterized, time-dependent partial differential equations (PDEs). The method employs Operator Inference, a scientific machine learning framework combining data-driven learning and physics-based modeling. The parametric structure of the governing equations is embedded directly into the reduced-order model, and parameterized reduced-order operators are learned via a data-driven linear regression problem. The result is a reduced-order mo...

#1Victor SinghH-Index: 3

#2Karen WillcoxH-Index: 48

Conditional reliability analysis in high dimensions based on controlled mixture importance sampling and information reuse

#1Max EhreH-Index: 4

#2Iason PapaioannouH-Index: 18

Last. Daniel StraubH-Index: 32

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Abstract In many contexts, it is of interest to assess the impact of selected parameters on the failure probability of a physical system. To this end, one can perform conditional reliability analysis, in which the probability of failure becomes a function of these parameters. Computing conditional reliability requires recomputing failure probabilities for a sample sequence of the parameters, which strongly increases the already high computational cost of conventional reliability analysis. We all...

#1Luwen HuangH-Index: 2

Last. Karen WillcoxH-Index: 48

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#1Omar Ghattas (University of Texas at Austin)H-Index: 57

#2Karen Willcox (University of Texas at Austin)H-Index: 48

This article addresses the inference of physics models from data, from the perspectives of inverse problems and model reduction. These fields develop formulations that integrate data into physics-based models while exploiting the fact that many mathematical models of natural and engineered systems exhibit an intrinsically low-dimensional solution manifold. In inverse problems, we seek to infer uncertain components of the inputs from observations of the outputs, while in model reduction we seek l...

#1Steven A. Niederer ('KCL': King's College London)H-Index: 1

#3Mark GirolamiH-Index: 65

#1Michael G. Kapteyn (MIT: Massachusetts Institute of Technology)H-Index: 5

#2Jacob V. R. PretoriusH-Index: 2

Last. Karen Willcox (University of Texas at Austin)H-Index: 48

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A unifying mathematical formulation is needed to move from one-off digital twins built through custom implementations to robust digital twin implementations at scale. This work proposes a probabilistic graphical model as a formal mathematical representation of a digital twin and its associated physical asset. We create an abstraction of the asset–twin system as a set of coupled dynamical systems, evolving over time through their respective state spaces and interacting via observed data and contr...

mfEGRA: Multifidelity efficient global reliability analysis through active learning for failure boundary location

#1Anirban Chaudhuri (MIT: Massachusetts Institute of Technology)H-Index: 9

#2Alexandre Noll Marques (MIT: Massachusetts Institute of Technology)H-Index: 8

Last. Karen Willcox (University of Texas at Austin)H-Index: 48

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This paper develops mfEGRA, a multifidelity active learning method using data-driven adaptively refined surrogates for failure boundary location in reliability analysis. This work addresses the issue of prohibitive cost of reliability analysis using Monte Carlo sampling for expensive-to-evaluate high-fidelity models by using cheaper-to-evaluate approximations of the high-fidelity model. The method builds on the efficient global reliability analysis (EGRA) method, which is a surrogate-based metho...

#1Mengwu GuoH-Index: 5

#2Shane A. McQuarrie (University of Texas at Austin)H-Index: 4

Last. Karen Willcox (University of Texas at Austin)H-Index: 48

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Close Researchers

Benjamin Peherstorfer

H-index : 22

Douglas Allaire

H-index : 16

Tan Bui-Thanh

H-index : 22

Omar Ghattas

H-index : 57

Boris Kramer

H-index : 13

Murali Damodaran

H-index : 12

Michael S. Eldred

H-index : 27

Youssef M. Marzouk

H-index : 32

Anirban Chaudhuri

H-index : 9

Theresa Robinson

H-index : 5

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