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Nonlinear Model Order Reduction via Lifting Transformations and Proper Orthogonal Decomposition

AIAA journal, 2019-06, Vol.57 (6), p.2297-2307

2019

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

SIAM review, 2018, Vol.60 (3), p.550-591

2018

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

Computers & fluids, 2019-01, Vol.179, p.704-717

2019

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Learning Nonlinear Reduced Models from Data with Operator Inference

Annual review of fluid mechanics, 2024-01, Vol.56 (1), p.521-548

2024

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Non-intrusive data-driven model reduction for differential–algebraic equations derived from lifting transformations

Computer methods in applied mechanics and engineering, 2022-02, Vol.389 (C), p.114296, Article 114296

2022

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Data-driven model reduction for the Bayesian solution of inverse problems

International journal for numerical methods in engineering, 2015-05, Vol.102 (5), p.966-990

2015

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Bayesian operator inference for data-driven reduced-order modeling

Computer methods in applied mechanics and engineering, 2022-12, Vol.402, p.115336, Article 115336

2022

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Information Reuse for Importance Sampling in Reliability-Based Design Optimization

Reliability engineering & system safety, 2020-09, Vol.201, p.106853, Article 106853

2020

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Data-driven reduced-order models via regularised Operator Inference for a single-injector combustion process

Journal of the Royal Society of New Zealand, 2021-04, Vol.51 (2), p.194-211

2021

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Decision-Making Under Uncertainty for a Digital Thread-Enabled Design Process

Journal of mechanical design (1990), 2021-09, Vol.143 (9)

2021

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The imperative of physics-based modeling and inverse theory in computational science

Nature Computational Science, 2021-03, Vol.1 (3), p.166-168

2021

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Multifidelity approaches for optimization under uncertainty

International journal for numerical methods in engineering, 2014-12, Vol.100 (10), p.746-772

2014

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Methodology for Path Planning with Dynamic Data-Driven Flight Capability Estimation

AIAA journal, 2017-08, Vol.55 (8), p.2727-2738

2017

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A probabilistic graphical model foundation for enabling predictive digital twins at scale

Nature Computational Science, 2021-05, Vol.1 (5), p.337-347

2021

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Localized Discrete Empirical Interpolation Method

SIAM journal on scientific computing, 2014-01, Vol.36 (1), p.A168-A192

2014

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

Computer methods in applied mechanics and engineering, 2021-08, Vol.381 (C), p.113826, Article 113826

2021

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Multifidelity Dimension Reduction via Active Subspaces

SIAM journal on scientific computing, 2020, Vol.42 (2), p.A929-A956

2020

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Domain Decomposition for Data-Driven Reduced Modeling of Large-Scale Systems

AIAA journal, 2024-11, p.1-16

2024

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Data to decisions: Real-time structural assessment from sparse measurements affected by uncertainty

Computers & structures, 2017-04, Vol.182, p.296-312

2017

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Design for additive manufacturing: cellular structures in early-stage aerospace design

Structural and multidisciplinary optimization, 2019-08, Vol.60 (2), p.411-428

2019

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

Computer methods in applied mechanics and engineering, 2016-07, Vol.306 (C), p.196-215

2016

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Influence of Transonic Flutter on the Conceptual Design of Next-Generation Transport Aircraft

AIAA journal, 2019-05, Vol.57 (5), p.1973-1987

2019

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Distributionally robust optimization for engineering design under uncertainty

International journal for numerical methods in engineering, 2019-11, Vol.120 (7), p.835-859

2019

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Design optimization using multiple dominance relations

International journal for numerical methods in engineering, 2020-06, Vol.121 (11), p.2481-2502

2020

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Learning physics-based models from data: perspectives from inverse problems and model reduction

Acta numerica, 2021-05, Vol.30, p.445-554

2021

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Nonlinear Model Order Reduction via Lifting Transformations and Proper Orthogonal Decomposition

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

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

Learning Nonlinear Reduced Models from Data with Operator Inference

Non-intrusive data-driven model reduction for differential–algebraic equations derived from lifting transformations

Data-driven model reduction for the Bayesian solution of inverse problems

Bayesian operator inference for data-driven reduced-order modeling

Information Reuse for Importance Sampling in Reliability-Based Design Optimization

Data-driven reduced-order models via regularised Operator Inference for a single-injector combustion process

Decision-Making Under Uncertainty for a Digital Thread-Enabled Design Process

The imperative of physics-based modeling and inverse theory in computational science

Multifidelity approaches for optimization under uncertainty

Methodology for Path Planning with Dynamic Data-Driven Flight Capability Estimation

A probabilistic graphical model foundation for enabling predictive digital twins at scale

Localized Discrete Empirical Interpolation Method

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

Multifidelity Dimension Reduction via Active Subspaces

Domain Decomposition for Data-Driven Reduced Modeling of Large-Scale Systems

Data to decisions: Real-time structural assessment from sparse measurements affected by uncertainty

Design for additive manufacturing: cellular structures in early-stage aerospace design

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

Influence of Transonic Flutter on the Conceptual Design of Next-Generation Transport Aircraft

Distributionally robust optimization for engineering design under uncertainty

Design optimization using multiple dominance relations

Learning physics-based models from data: perspectives from inverse problems and model reduction

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