Karen E. Willcox
Professor
Develops scientific machine learning methods, including data-driven model reduction and multi-fidelity methods for digital twins of engineered systems.
Karen E. Willcox's lab at UT Austin works in AI for Science. 44 papers since 2023, mostly npj Digital Medicine. 5 PhD students. 69 alumni. Applications close .
At a glance
- papers, 2023–2026
- 11/ year
- at T1 venues
- 0%
- PhD students
- 5
- MS students
- 0
- alumni listed
- 69
Papers by yearfrom the lab homepage, 2023–2026
- 12 papers2026 · T1 0 · collecting
- 10 papers2025 · T1 0
- 11 papers2024 · T1 0
- 11 papers2023 · T1 0
T1 venuesOtherVenues: npj Digital Medicine
Research focuspaper titles that use each topic's words
- twins9 papers
- reduction4 papers
- model reduction3 papers
- develops scientific0 papers
- scientific machine0 papers
Paper list
From the lab homepage.
202612 papers
- AIAA JournalBlock-structured Operator Inference for coupled multiphysics model reduction (opens in a new tab)
- In Proceedings of AIAA SciTech Forum & Exhibition, Orlando, FLFederated digital twins for space systems (opens in a new tab)
- Journal of Computational PhysicsFormal verification of digital twins with the temporal logic of actions (opens in a new tab)
- International Learning Analytics and Knowledge Conference (LAK), Bergen, NorwayModeling longitudinal student pathways with explainable generative models (opens in a new tab)
- In Proceedings of AIAA SciTech Forum & Exhibition, Orlando, FLMulti-resolution digital twins under uncertainty with applications to multiscale systems (opens in a new tab)
- Advances in Computational MathematicsNested operator inference for adaptive data-driven learning of reduced-order models (opens in a new tab)
- In Proceedings of AIAA SciTech Forum & Exhibition, Orlando, FLNeural operator-enabled aerodynamic load estimation for hypersonics (opens in a new tab)
- Journal of Computational PhysicsPredictive digital twins with quantified uncertainty for patient-specific decision making in oncology (opens in a new tab)
- To appear, SIAM/ASA Journal on Uncertainty QuantificationQuasi-Monte Carlo methods for uncertainty quantification of tumor growth modeled by a parametric semi-linear parabolic reaction-diffusion equation (opens in a new tab)
- Computer Methods in Applied Mechanics and EngineeringRobust Topology Optimization of Continuum Structures under Material Uncertainties Using Multifidelity Monte Carlo
- BMC Medical Informatics and Decision MakingTumorTwin: A python framework for patient-specific digital twins in oncology (opens in a new tab)
- In Model Validation and Uncertainty Quantification in Biomechanics: From Soft Biological Tissue to Blood Flow, ElsevierValidating the predictions of mathematical models describing tumor growth and treatment response (opens in a new tab)
202510 papers
- In Proceedings of AIAA SciTech Forum & Exhibition, Orlando, FLA parallel implementation of reduced-order modeling of large-scale systems
- Physica D: Nonlinear PhenomenaBayesian learning with Gaussian processes for low-dimensional representations of time-dependent nonlinear systems (opens in a new tab)
- In Proceedings of AIAA SciTech Forum & Exhibition, Orlando, FLDigital-twin-enabled multi-spacecraft on-orbit operations
- Computer Physics CommunicationsDistributed computing for physics-based data-driven reduced modeling at scale: Application to a rotating detonation rocket engine (opens in a new tab)
- In Proceedings of AIAA SciTech Forum & Exhibition, Orlando, FLImproving neural network efficiency with multifidelity and dimensionality reduction techniques
- Computational GeosciencesMultifidelity uncertainty quantification for ice sheet simulations (opens in a new tab)
- Computer Methods in Applied Mechanics and EngineeringNon-intrusive reduced-order modeling for dynamical systems with spatially localized features (opens in a new tab)
- Machine Learning for Computational Science and EngineeringProjection-based multifidelity linear regression for data-scarce applications (opens in a new tab)
- AIAA JournalReal-time aerodynamic load estimation for hypersonics via strain-based inverse maps (opens in a new tab)
- npj Digital MedicineSurvey and perspective on verification, validation, and uncertainty quantification of digital twins for precision medicine (opens in a new tab)
202411 papers
- Computer Methods in Applied Mechanics and EngineeringA digital twin framework for civil engineering structures (opens in a new tab)
- In Physics-based and Data-driven Modeling for Digital Twins, Editors K. Cherifi and I.V. Gosea, SEMA SIMAI Springer SeriesAdaptive planning for risk-aware predictive digital twins (opens in a new tab)
- IEEE DigitalTwinDigital twin: Graph formulations for managing complexity and uncertainty (opens in a new tab)
- Nature Computational ScienceDigital twins in mechanical and aerospace engineering (opens in a new tab)
- AIAA JournalDomain decomposition for data-driven reduced modeling of large-scale systems (opens in a new tab)
- IEEE International Conference on Big Data, Washington D.C.Educational Digital Twin: Tackling complexity in educational big data (opens in a new tab)
- IEEE Conference on Decision and Control (CDC), Milan, ItalyEnforcing structure in data-driven reduced modeling through nested operator inference
- Annual Review of Fluid MechanicsLearning nonlinear reduced models from data with Operator Inference (opens in a new tab)
- ChaosLearning physics-based reduced-order models from data using nonlinear manifolds (opens in a new tab)
- Computers and StructuresMultifidelity methods for uncertainty quantification of a nonlocal model for phase changes in materials
- Nature Computational ScienceThe role of computational science in digital twins (opens in a new tab)
202311 papers
- In Proceedings of AIAA Aviation Forum, San Diego, CAData-driven closures for the dynamic mode decomposition using quadratic manifolds (opens in a new tab)
- In Proceedings of AIAA SciTech Forum & Exhibition, National Harbor, MDData-driven model reduction via operator inference for coupled aeroelastic flutter (opens in a new tab)
- In: Darema, F., Blasch, E.P., Ravela, S., Aved, A.J. (eds) Handbook of Dynamic Data Driven Applications Systems. Springer, ChamFrom Data to Decisions: A Real-Time Measurement–Inversion–Prediction–Steering Framework for Hazardous Events and Health Monitoring
- IEEE Conference on Decision and Control (CDC), SingaporeLearning latent representations in high-dimensional state spaces using polynomial manifold constructions (opens in a new tab)
- Computer Methods in Applied Mechanics and EngineeringMulti-output multilevel best linear unbiased estimators via semidefinite programming (opens in a new tab)
- In Proceedings of AIAA SciTech Forum & Exhibition, National Harbor, MDMultifidelity robust topology optimization for material uncertainties with digital manufacturing
- SIAM Journal on Scientific ComputingNon-intrusive reduced-order models for parametric partial differential equations via data-driven operator inference (opens in a new tab)
- Computer Methods in Applied Mechanics and EngineeringOperator inference for non-intrusive model reduction with quadratic manifolds (opens in a new tab)
- In Proceedings of AIAA SciTech Forum & Exhibition, National Harbor, MDParametric non-intrusive reduced-order models via operator inference for large-scale rotating detonation engine simulations (opens in a new tab)
- Frontiers in Artificial Intelligence, Special Issue on Digital Twins in OncologyPredictive digital twin for optimizing patient-specific radiotherapy regimens under uncertainty in high-grade gliomas (opens in a new tab)
- In Proceedings of AIAA SciTech Forum & Exhibition, National Harbor, MDProjection-based multifidelity linear regression for data-poor applications (opens in a new tab)
Where alumni went69 listed
Apply to UT AustinFall 2027
Computer Science PhD
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- Single round (Dec 15)
- Opens
- GRE
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- English
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- Fee
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Not yet confirmed for this intake.
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