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We present a novel active learning algorithm, termed as iterative surrogate model optimization (ISMO), for robust and efficient numerical approximation of PDE constrained optimization problems.
Practical methods of optimization
R. Fletcher · 1987
Earlier work this paper cites.
A compressible Navier-Stokes solver with two-equation and Reynolds stress turbulence closure models
J. H. Morrison · 1992
Earlier work this paper cites.
Aerodynamic shape optimization of wing and wing-body configurations using control theory
J. Reuther and A. Jameson · 1995
Earlier work this paper cites.
Monte carlo and quasi-monte carlo methods
R. E. Caflisch · 1998
Earlier work this paper cites.
Artificial neural networks for solving ordinary and partial differential equations
I. E. Lagaris, A. Likas, and D. I. Fotiadis · 1998
Earlier work this paper cites.
Survey of shape parameterization techniques for high-fidelity multidisciplinary shape optimization
J. A. Samareh · 2001
Earlier work this paper cites.
On the mathematical foundations of learning
F. Cucker and S. Smale · 2002
Earlier work this paper cites.
Gaussian processes in machine learning
C. E. Rasmussen · 2003
Earlier work this paper cites.
Particle swarm optimization
M. Clerc · 2005
Earlier work this paper cites.
Multidimensional variation for quasi-monte carlo
A. B. Owen · 2005
Earlier work this paper cites.
Efficient numerical solution of parabolic optimization problems by finite element methods
R. Becker, D. Meidner, and B. Vexler · 2007
Earlier work this paper cites.
A mesh deformation strategy for multiblock structured grids
K. Kumar and M. T. Nair · 2007
Earlier work this paper cites.
Engineering Design via Surrogate Modelling: A Practical Guide
A. I. J. Forrester, A. Sóbester, and A. J. Keane · 2008
Earlier work this paper cites.
Introduction to genetic algorithms
S. N. Sivanandam and S. N. Deepa · 2008
Earlier work this paper cites.
Applied Shape Optimization for Fluids
B. Mohammadi and O. Pironneau · 2009
Earlier work this paper cites.
Optimal control of partial differential equations
F. Troltzsch · 2010
Cited alongside, same era.
Kriging-based optimization applied to flow control
R. Duvigneau and P. Chandrashekar · 2011
Cited alongside, same era.
Efficient shape optimization for certain and uncertain aerodynamic design
C. Schillings, S. Schmidt, and V. Schulz · 2011
Cited alongside, same era.
Computational optimization of systems governed by partial differential equations
A. Borzi and V. Schultz · 2012
Cited alongside, same era.
Active learning: synthesis lectures on artificial intelligence and machine learning
B. Settles · 2012
Cited alongside, same era.
TensorFlow: Large-scale machine learning on heterogeneous systems, 2015
M. Abadi, A. Agarwal, P. Barham, E. Brevdo, Z. Chen, C. Citro, G. S. Corrado, A. Davis, J. Dean, M. Devin, S. Ghemawat, I. Goodfellow, A. Harp, G. Irving, M. Isard, Y. Jia, R. Jozefowicz, L. Kaiser, M. Kudlur, J. Levenberg, D. Mané, R. Monga, S. Moore, D. Murray, C. Olah, M. Schuster, J. Shlens, B. Steiner, I. Sutskever, K. Talwar, P. Tucker, V. Vanhoucke, V. Vasudevan, F. Viégas, O. Vinyals, P. Warden, M. Wattenberg, M. Wicke, Y. Yu, and X. Zheng · 2015
Solving high-dimensional partial differential equations using deep learning
J. Han, A. Jentzen, and W. E · 2018
Later among the works it cites.
Hidden physics models: Machine learning of nonlinear partial differential equations
M. Raissi and G. E. Karniadakis · 2018
Later among the works it cites.
M. Raissi, A. Yazdani, and G. E. Karniadakis · 2018
Later among the works it cites.
An artificial neural network as a troubled-cell indicator
D. Ray and J. S. Hesthaven · 2018
Later among the works it cites.
Consistency of lipschitz learning with infinite unlabeled data and finite labeled data,
J. Calder · 2019
Later among the works it cites.
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Aerodynamic shape optimization investigations of the common research model wing benchmark
Z. Lyu, G. K. W. Kenway, and J. R. R. A. Martins · 2015
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Reduced basis methods for partial differential equations: an introduction
A. Quarteroni, A. Manzoni, and F. Negri · 2015
Cited alongside, same era.
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Cited alongside, same era.
Deep learning-based numerical methods for high-dimensional parabolic partial differential equations and backward stochastic differential equations
W. E, J. Han, and A. Jentzen · 2017
Cited alongside, same era.
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Later among the works it cites.
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Later among the works it cites.
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Ibm spectrum ldf, 2020 (accessed July 27, 2020)
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P. Virtanen, R. Gommers, T. E. Oliphant, M. Haberland, T. Reddy, D. Cournapeau, E. Burovski, P. Peterson, W. Weckesser, J. Bright, S. J. van der Walt, M. Brett, J. Wilson, K. Jarrod Millman, N. Mayorov, A. R. J. Nelson, E. Jones, R. Kern, E. Larson, C. Carey, İ. Polat, Y. Feng, E. W. Moore, J. Vand erPlas, D. Laxalde, J. Perktold, R. Cimrman, I. Henriksen, E. A. Quintero, C. R. Harris, A. M. Archibald, A. H. Ribeiro, F. Pedregosa, P. van Mulbregt, and S. . . Contributors · 2020
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