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1932
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2001
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2003
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2003
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2004
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S. Skogestad and I. Postlethwaite,
2005
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J. Quiñonero-Candela and C. E. Rasmussen, “A unifying view of sparse approximate Gaussian process regression,”
2005
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W. Johnson, “Model for vortex ring state influence on rotorcraft flight dynamics,” 2005
2005
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J. Harding, M. Shahbaz, A. Kusiak
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J. Nocedal and S. Wright,
2006
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T. Sarlos, “Improved approximation algorithms for large matrices via random projections,” in
2006
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K. Willcox, “Unsteady flow sensing and estimation via the gappy proper orthogonal decomposition,”
2006
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E. J. Candès
2006
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D. L. Donoho, “Compressed sensing,”
2006
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E. J. Candès and T. Tao, “Near optimal signal recovery from random projections: Universal encoding strategies?”
2006
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E. J. Candès, J. Romberg, and T. Tao, “Robust uncertainty principles: exact signal reconstruction from highly incomplete frequency information,”
2006
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T. Bui-Thanh, K. Willcox, O. Ghattas, and B. van Bloemen Waanders, “Goal-oriented, model-constrained optimization for reduction of large-scale systems,”
2007
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R. G. Baraniuk, “Compressive sensing,”
2007
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J. Bongard and H. Lipson, “Automated reverse engineering of nonlinear dynamical systems,”
2007
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C. Lynch, “Big data: How do your data grow?”
2008
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X. Wu, V. Kumar, J. R. Quinlan, J. Ghosh, Q. Yang, H. Motoda, G. J. McLachlan, A. Ng, B. Liu, S. Y. Philip
2008
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T. Bui-Thanh, K. Willcox, and O. Ghattas, “Model reduction for large-scale systems with high-dimensional parametric input space,”
2008
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K. Bowcutt, G. Kuruvila, T. A. Grandine, T. A. Hogan, and E. J. Cramer, “Advancements in multidisciplinary design optimization applied to hypersonic vehicles to achieve closure,” in
2008
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B. J. Marsh, “Laser tracker assisted aircraft machining and assembly,” SAE Technical Paper, Tech. Rep., 2008
2008
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A. J. Hey, S. Tansley, K. M. Tolle
2009
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J. Deng, W. Dong, R. Socher, L.-J. Li, K. Li, and L. Fei-Fei, “Imagenet: A large-scale hierarchical image database,” in
2009
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S. Joshi and S. Boyd, “Sensor selection via convex optimization,”
2009
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R. T. Rockafellar and R. J.-B. Wets,
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V. Rokhlin, A. Szlam, and M. Tygert, “A randomized algorithm for principal component analysis,”
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2009
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B. Settles, “Active learning literature survey,” University of Wisconsin-Madison Department of Computer Sciences, Tech. Rep., 2009
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P. J. Schmid, “Dynamic mode decomposition of numerical and experimental data,”
2010
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B. J. McKeon and A. S. Sharma, “A critical layer model for turbulent pipe flow,”
2010
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L. Bottou, “Large-scale machine learning with stochastic gradient descent,” in
2010
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S. Chaturantabut and D. C. Sorensen, “Nonlinear model reduction via discrete empirical interpolation,”
2010
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A. Blom, “Structural performance of fiber-placed, variable-stiffness composite conical and cylindrical shells,”
2010
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J. Jamshidi, A. Kayani, P. Iravani, P. G. Maropoulos, and M. Summers, “Manufacturing and assembly automation by integrated metrology systems for aircraft wing fabrication,”
2010
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J. E. Muelaner and P. G. Maropoulos, “Design for measurement assisted determinate assembly (MADA) of large composite structures,” in
2010
Cited alongside, same era.
B. J. Marsh, T. Vanderwiel, K. VanScotter, and M. Thompson, “Method for fitting part assemblies,” Jul. 13 2010
2010
Cited alongside, same era.
P. L. Combettes and J.-C. Pesquet, “Proximal splitting methods in signal processing,” in
2011
Cited alongside, same era.
B. W. Brunton, S. L. Brunton, J. L. Proctor, and J. N. Kutz, “Sparse sensor placement optimization for classification,”
2016
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T. Du, A. Schulz, B. Zhu, B. Bickel, and W. Matusik, “Computational multicopter design,” 2016
2016
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K. Taira, S. L. Brunton, S. Dawson, C. W. Rowley, T. Colonius, B. J. McKeon, O. T. Schmidt, S. Gordeyev, V. Theofilis, and L. S. Ukeiley, “Modal analysis of fluid flows: An overview,”
2017
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K. Carlberg, M. Barone, and H. Antil, “Galerkin v. least-squares Petrov–Galerkin projection in nonlinear model reduction,”
2017
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A. P. Singh, S. Medida, and K. Duraisamy, “Machine-learning-augmented predictive modeling of turbulent separated flows over airfoils,”
2017
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M. W. Mahoney, “Randomized algorithms for matrices and data,”
2011
Cited alongside, same era.
N. Halko, P. G. Martinsson, and J. A. Tropp, “Finding structure with randomness: Probabilistic algorithms for constructing approximate matrix decompositions,”
2011
Cited alongside, same era.
P.-G. Martinsson, V. Rokhlin, and M. Tygert, “A randomized algorithm for the decomposition of matrices,”
2011
Cited alongside, same era.
N. Halko, P.-G. Martinsson, Y. Shkolnisky, and M. Tygert, “An algorithm for the principal component analysis of large data sets,”
2011
Cited alongside, same era.
E. J. Candès, X. Li, Y. Ma, and J. Wright, “Robust principal component analysis?”
2011
Cited alongside, same era.
J. E. Muelaner, A. Kayani, O. Martin, and P. Maropoulos, “Measurement assisted assembly and the roadmap to part-to-part assembly,” in
2011
Cited alongside, same era.
B. Chouvion, A. Popov, S. Ratchev, C. Mason, and M. Summers, “Interface management in wing-box assembly,” SAE Technical Paper, Tech. Rep., 2011
2011
Cited alongside, same era.
A. Aravkin, J. V. Burke, L. Ljung, A. Lozano, and G. Pillonetto, “Generalized Kalman smoothing: Modeling and algorithms,”
2017
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J. A. Duersch and M. Gu, “Randomized QR with column pivoting,”
2017
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M. Grieves and J. Vickers, “Digital twin: Mitigating unpredictable, undesirable emergent behavior in complex systems,” in
2017
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K. Manohar, S. L. Brunton, and J. N. Kutz, “Environmental identification in flight using sparse approximation of wing strain,”
2017
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S. L. Brunton, B. W. Brunton, J. L. Proctor, E. Kaiser, and J. N. Kutz, “Chaos as an intermittently forced linear system,”
2017
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B. Conduit, N. Jones, H. Stone, and G. Conduit, “Design of a nickel-base superalloy using a neural network,”
2017
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R. S. Sutton and A. G. Barto,
2018
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D. Silver, T. Hubert, J. Schrittwieser, I. Antonoglou, M. Lai, A. Guez, M. Lanctot, L. Sifre, D. Kumaran, T. Graepel
2018
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J.-C. Loiseau and S. L. Brunton, “Constrained sparse Galerkin regression,”
2018
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M. Raissi and G. E. Karniadakis, “Hidden physics models: Machine learning of nonlinear partial differential equations,”
2018
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J.-C. Loiseau, B. R. Noack, and S. L. Brunton, “Sparse reduced-order modeling: sensor-based dynamics to full-state estimation,”
2018
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2018
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2018
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K. Manohar, B. W. Brunton, J. N. Kutz, and S. L. Brunton, “Data-driven sparse sensor placement for reconstruction: Demonstrating the benefits of exploiting known patterns,”
2018
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L. Bottou, F. E. Curtis, and J. Nocedal, “Optimization methods for large-scale machine learning,”
2018
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D. Davis, D. Drusvyatskiy, S. Kakade, and J. D. Lee, “Stochastic subgradient method converges on tame functions,”
2018
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F. Tao, J. Cheng, Q. Qi, M. Zhang, H. Zhang, and F. Sui, “Digital twin-driven product design, manufacturing and service with big data,”
2018
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S. Singh, E. Shebab, N. Higgins, K. Fowler, T. Tomiyama, and C. Fowler, “Challenges of digital twin in high value manufacturing,”
2018
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K. Manohar, T. Hogan, J. Buttrick, A. G. Banerjee, J. N. Kutz, and S. L. Brunton, “Predicting shim gaps in aircraft assembly with machine learning and sparse sensing,”
2018
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P. Verpoort, P. MacDonald, and G. J. Conduit, “Materials data validation and imputation with an artificial neural network,”
2018
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B. Conduit, N. G. Jones, H. J. Stone, and G. J. Conduit, “Probabilistic design of a molybdenum-base alloy using a neural network,”
2018
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A. G. Green, G. Conduit, and F. Krüger, “Quantum order-by-disorder in strongly correlated metals,”
2018
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S. L. Brunton and J. N. Kutz,
2019
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K. Duraisamy, G. Iaccarino, and H. Xiao, “Turbulence modeling in the age of data,”
2019
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S. L. Brunton and J. N. Kutz, “Methods for data-driven multiscale model discovery for materials,”
2019
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M. Raissi, P. Perdikaris, and G. Karniadakis, “Physics-informed neural networks: A deep learning framework for solving forward and inverse problems involving nonlinear partial differential equations,”
2019
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F. Noé, S. Olsson, J. Köhler, and H. Wu, “Boltzmann generators: Sampling equilibrium states of many-body systems with deep learning,”
2019
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2019
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M. D. Cranmer, R. Xu, P. Battaglia, and S. Ho, “Learning symbolic physics with graph networks,”
2019
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A. Aravkin and D. Davis, “Trimmed statistical estimation via variance reduction,”
2019
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D. Davis and D. Drusvyatskiy, “Stochastic model-based minimization of weakly convex functions,”
2019
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P. Zheng, T. Askham, S. L. Brunton, J. N. Kutz, and A. Y. Aravkin, “Sparse relaxed regularized regression: SR3,”
2019
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N. B. Erichson, S. Voronin, S. L. Brunton, and J. N. Kutz, “Randomized matrix decompositions using R,”
2019
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N. B. Erichson, L. Mathelin, J. N. Kutz, and S. L. Brunton, “Randomized dynamic mode decomposition,”
2019
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A. Alla and J. N. Kutz, “Randomized model order reduction,”
2019
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A. Rasheed, O. San, and T. Kvamsdal, “Digital twin: Values, challenges and enablers,”
2019
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2019
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S. L. Brunton, B. R. Noack, and P. Koumoutsakos, “Machine learning for fluid mechanics,”
2020
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M. Raissi, A. Yazdani, and G. E. Karniadakis, “Hidden fluid mechanics: Learning velocity and pressure fields from flow visualizations,”
2020
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2020
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2020
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K. Taira, M. S. Hemati, S. L. Brunton, Y. Sun, K. Duraisamy, S. Bagheri, S. T. Dawson, and C.-A. Yeh, “Modal analysis of fluid flows: Applications and outlook,”
2020
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S. A. Renganathan, “Koopman-based approach to nonintrusive reduced order modeling: Application to aerodynamic shape optimization and uncertainty propagation,”
2020
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N. B. Erichson, K. Manohar, S. L. Brunton, and J. N. Kutz, “Randomized CP tensor decomposition,”
2020
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Z. Bai, E. Kaiser, J. L. Proctor, J. N. Kutz, and S. L. Brunton, “Dynamic mode decomposition for compressive system identification,”
2020
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F. Chinesta, E. Cueto, E. Abisset-Chavanne, J. L. Duval, and F. El Khaldi, “Virtual, digital and hybrid twins: a new paradigm in data-based engineering and engineered data,”
2020
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N. Bons and J. Martins, “Aerostructural wing design exploration with multidisciplinary design optimization,” in
2020
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P. Juarez, E. Gregory, and K. Cramer, “In situ thermal inspection of automated fiber placement manufacturing,”
2020
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C. Sacco, A. Radwan, T. Beatty, and R. Harik, “Machine learning based AFP inspection: A tool for characterization and integration,”
2020
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J. Wright, A. Ganesh, S. Rao, Y. Peng, and Y. Ma, “Robust principal component analysis: Exact recovery of corrupted low-rank matrices via convex optimization,” in
2088
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