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Many applications, such as in physical simulation and engineering design, demand we estimate functions with high-dimensional outputs.
Multi-fidelity bayesian optimization with max-value entropy search
Takeno, S., Fukuoka, H., Tsukada, Y., Koyama, T., Shiga, M., Takeuchi, I., and Karasuyama, M. (2019) · 1901
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Deep Gaussian processes for multi-fidelity modeling
Cutajar, K., Pullin, M., Damianou, A., Lawrence, N., and González, J. (2019) · 1903
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Discriminative active learning
Gissin, D. and Shalev-Shwartz, S. (2019) · 1907
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Electrostatic capacity, heat flow, and brownian motion
Spitzer, F. (1964) · 1964
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Numerical solution of the navier-stokes equations
Chorin, A. J. (1968) · 1968
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The pricing of options and corporate liabilities
Black, F. and Scholes, M. (1973) · 1973
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Numerical study of viscous flow in a cavity
Bozeman, J. D. and Dalton, C. (1973) · 1973
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Two calculation procedures for steady, three-dimensional flows with recirculation
Caretto, L., Gosman, A., Patankar, S., and Spalding, D. (1973) · 1973
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The laplace and poisson equations in schwarzschild’s space-time
Persides, S. (1973) · 1973
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The finite element method
Zienkiewicz, O. C., Taylor, R. L., Zienkiewicz, O. C., and Taylor, R. L. (1977) · 1977
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Mining geostatistics
Journel, A. G. and Huijbregts, C. J. (1978) · 1978
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Design and analysis of computer experiments
Sacks, J., Welch, W. J., Mitchell, T. J., and Wynn, H. P. (1989) · 1989
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Burgers equation with a fractional derivative; hereditary effects on nonlinear acoustic waves
Sugimoto, N. (1991) · 1991
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A note on the delta method
Oehlert, G. W. (1992) · 1992
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Particle hopping models and traffic flow theory
Nagel, K. (1996) · 1996
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On the design of compliant mechanisms using topology optimization
Sigmund, O. (1997) · 1997
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Nonlinear component analysis as a kernel eigenvalue problem
Schölkopf, B., Smola, A., and Müller, K.-R. (1998) · 1998
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Numerical solution of one-dimensional burgers equation: explicit and exact-explicit finite difference methods
Kutluay, S., Bahadir, A., and Özde c · 1999
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Predicting the output from a complex computer code when fast approximations are available
Kennedy, M. C. and O’Hagan, A. (2000) · 2000
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The isomap algorithm and topological stability
Balasubramanian, M. and Schwartz, E. L. (2002) · 2002
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Infinite mixtures of gaussian process experts
Rasmussen, C. E. and Ghahramani, Z. (2002) · 2002
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The heat equation and reflected brownian motion in time-dependent domains
Burdzy, K., Chen, Z.-Q., Sylvester, J., et al. (2004) · 2004
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Sequential kriging optimization using multiple-fidelity evaluations
Huang, D., Allen, T. T., Notz, W. I., and Miller, R. A. (2006) · 2006
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Margin based active learning
Balcan, M.-F., Broder, A., and Zhang, T. (2007) · 2007
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An introduction to computational fluid dynamics: the finite volume method
Versteeg, H. K. and Malalasekera, W. (2007) · 2007
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Computer model calibration using high-dimensional output
Higdon, D., Gattiker, J., Williams, B., and Rightley, M. (2008) · 2008
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Active learning with real annotation costs
Settles, B., Craven, M., and Friedland, L. (2008) · 2008
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Graphical models, exponential families, and variational inference
Wainwright, M. J., Jordan, M. I., et al. (2008) · 2008
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Agnostic active learning
Balcan, M.-F., Beygelzimer, A., and Langford, J. (2009) · 2009
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A critical review of established methods of structural topology optimization
Rozvany, G. I. (2009) · 2009
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Active learning literature survey
Settles, B. (2009) · 2009
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Numerical methods for engineers
Chapra, S. C., Canale, R. P., et al. (2010) · 2010
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Dropout as a Bayesian approximation: Representing model uncertainty in deep learning
Gal, Y. and Ghahramani, Z. (2016) · 2016
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Gaussian process bandit optimisation with multi-fidelity evaluations
Kandasamy, K., Dasarathy, G., Oliva, J. B., Schneider, J., and Póczos, B. (2016) · 2016
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Manifold learning for the emulation of spatial fields from computational models
Xing, W., Triantafyllidis, V., Shah, A., Nair, P., and Zabaras, N. (2016) · 2016
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Deep Bayesian active learning with image data
Gal, Y., Islam, R., and Ghahramani, Z. (2017) · 2017
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Deep active learning over the long tail
Geifman, Y. and El-Yaniv, R. (2017) · 2017
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Chung, T. (2010) · 2010
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Bayesian emulation of complex multi-output and dynamic computer models
Conti, S. and O’Hagan, A. (2010) · 2010
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Two faces of active learning
Dasgupta, S. (2011) · 2011
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Bayesian active learning for classification and preference learning
Houlsby, N., Huszár, F., Ghahramani, Z., and Lengyel, M. (2011) · 2011
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Numerical solution of partial differential equations
Olsen-Kettle, L. (2011) · 2011
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Bayesian approach to global optimization: theory and applications
Mockus, J. (2012) · 2012
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Kandasamy, K., Dasarathy, G., Schneider, J., and Póczos, B. (2017) · 2017
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Practical bayesian optimization for variable cost objectives
McLeod, M., Osborne, M. A., and Roberts, S. J. (2017) · 2017
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Nonlinear information fusion algorithms for data-efficient multi-fidelity modelling
Perdikaris, P., Raissi, M., Damianou, A., Lawrence, N., and Karniadakis, G. E. (2017) · 2017
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Multi-information source optimization
Poloczek, M., Wang, J., and Frazier, P. (2017) · 2017
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Raissi, M., Perdikaris, P., and Karniadakis, G. E. (2017) · 2017
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Reduced-order modelling of parameter-dependent, linear and nonlinear dynamic partial differential equation models
Shah, A., Xing, W., and Triantafyllidis, V. (2017) · 2017
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Max-value entropy search for efficient bayesian optimization
Wang, Z. and Jegelka, S. (2017) · 2017
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Continuous-fidelity bayesian optimization with knowledge gradient
Wu, J. and Frazier, P. I. (2017) · 2017
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Adversarial active learning for deep networks: a margin based approach
Ducoffe, M. and Precioso, F. (2018) · 2018
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Parametric topology optimization with multi-resolution finite element models
Keshavarzzadeh, V., Kirby, R. M., and Narayan, A. (2018) · 2018
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Active learning for convolutional neural networks: A core-set approach
Sener, O. and Savarese, S. (2018) · 2018
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Deep batch active learning by diverse, uncertain gradient lower bounds
Ash, J. T., Zhang, C., Krishnamurthy, A., Langford, J., and Agarwal, A. (2019) · 2019
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Multi-resolution multi-task gaussian processes
Hamelijnck, O., Damoulas, T., Wang, K., and Girolami, M. (2019) · 2019
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Batchbald: Efficient and diverse batch acquisition for deep bayesian active learning
Kirsch, A., van Amersfoort, J., and Gal, Y. (2019) · 2019
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Pytorch: An imperative style, high-performance deep learning library
Paszke, A., Gross, S., Massa, F., Lerer, A., Bradbury, J., Chanan, G., Killeen, T., Lin, Z., Gimelshein, N., Antiga, L., et al. (2019) · 2019
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Physics-informed neural networks: A deep learning framework for solving forward and inverse problems involving nonlinear partial differential equations
Raissi, M., Perdikaris, P., and Karniadakis, G. E. (2019) · 2019
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A general framework for multi-fidelity bayesian optimization with gaussian processes
Song, J., Chen, Y., and Yue, Y. (2019) · 2019
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Scalable high-order gaussian process regression
Zhe, S., Xing, W., and Kirby, R. M. (2019) · 2019
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Multi-fidelity bayesian optimization via deep neural networks
Li, S., Xing, W., Kirby, R., and Zhe, S. (2020) · 2020
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Multi-fidelity high-order gaussian processes for physical simulation
Wang, Z., Xing, W., Kirby, R., and Zhe, S. (2021) · 2021
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