Surrogate model (English Wikipedia)

Analysis of information sources in references of the Wikipedia article "Surrogate model" in English language version.

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11doi.org

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3ieee.org

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3harvard.edu

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3nih.gov

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2arxiv.org

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2semanticscholar.org

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1worldcat.org

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1springerprofessional.de

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1handle.net

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1inria.fr

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1ressources-actuarielles.net

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1umich.edu

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1zenodo.org

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1sciencedirect.com

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arxiv.org

Ranftl, Sascha; von der Linden, Wolfgang (2021-11-13). "Bayesian Surrogate Analysis and Uncertainty Propagation". Physical Sciences Forum. 3 (1): 6. arXiv:2101.04038. doi:10.3390/psf2021003006. ISSN 2673-9984.
Bliek, Laurens; Verstraete, Hans R. G. W.; Verhaegen, Michel; Wahls, Sander (2018). "Online Optimization with Costly and Noisy Measurements Using Random Fourier Expansions". IEEE Transactions on Neural Networks and Learning Systems. 29 (1): 167–182. arXiv:1603.09620. doi:10.1109/TNNLS.2016.2615134. PMID 27831891.

doi.org

Ranftl, Sascha; von der Linden, Wolfgang (2021-11-13). "Bayesian Surrogate Analysis and Uncertainty Propagation". Physical Sciences Forum. 3 (1): 6. arXiv:2101.04038. doi:10.3390/psf2021003006. ISSN 2673-9984.
Bandler, J.W.; Cheng, Q.S.; Dakroury, S.A.; Mohamed, A.S.; Bakr, M.H.; Madsen, K.; Sondergaard, J. (2004). "Space Mapping: The State of the Art". IEEE Transactions on Microwave Theory and Techniques. 52 (1): 337–361. Bibcode:2004ITMTT..52..337B. doi:10.1109/TMTT.2003.820904.
Cardenas, IC (2019). "On the use of Bayesian networks as a meta-modeling approach to analyse uncertainties in slope stability analysis". Georisk: Assessment and Management of Risk for Engineered Systems and Geohazards. 13 (1): 53–65. Bibcode:2019GAMRE..13...53C. doi:10.1080/17499518.2018.1498524. S2CID 216590427.
Manzoni, Luca; Papetti, Daniele M.; Cazzaniga, Paolo; Spolaor, Simone; Mauri, Giancarlo; Besozzi, Daniela; Nobile, Marco S. (2020). "Surfing on Fitness Landscapes: A Boost on Optimization by Fourier Surrogate Modeling". Entropy. 22 (3): 285. Bibcode:2020Entrp..22..285M. doi:10.3390/e22030285. PMC 7516743. PMID 33286059.
Bliek, Laurens; Verstraete, Hans R. G. W.; Verhaegen, Michel; Wahls, Sander (2018). "Online Optimization with Costly and Noisy Measurements Using Random Fourier Expansions". IEEE Transactions on Neural Networks and Learning Systems. 29 (1): 167–182. arXiv:1603.09620. doi:10.1109/TNNLS.2016.2615134. PMID 27831891.
Rayas-Sanchez, Jose E. (2016). "Power in Simplicity with ASM: Tracing the Aggressive Space Mapping Algorithm over Two Decades of Development and Engineering Applications". IEEE Microwave Magazine. 17 (4): 64–76. doi:10.1109/MMM.2015.2514188. hdl:11117/5948.
Jones, D. R. (2001). "A taxonomy of global optimization methods based on response surfaces" (PDF). Journal of Global Optimization. 21 (4): 345–383. doi:10.1023/A:1012771025575.
Bouhlel, M.A.; Hwang, J.H.; Bartoli, Nathalie; Lafage, R.; Morlier, J.; Martins, J.R.R.A. (2019). "A Python surrogate modeling framework with derivatives". Advances in Engineering Software. 135: 102662. doi:10.1016/j.advengsoft.2019.03.005. S2CID 128324330.
Kernc (2024), SAMBO: Sequential And Model-Based Optimization: Efficient global optimization in Python, doi:10.5281/zenodo.14461363
Jin, Y-C. (2011). "Surrogate-assisted evolutionary computation: Recent advances and future challenges". Swarm and Evolutionary Computation. 1 (2): 61–70. doi:10.1016/j.swevo.2011.05.001.
Wei, F-F.; Chen, W-N.; Yang, Q.; Deng, J.D.; Luo, X.; Jin, H.; Zhang, J. (2021). "A Classifier-Assisted Level-Based Learning Swarm Optimizer for Expensive Optimization". IEEE Transactions on Evolutionary Computation. 25 (2): 219–233. doi:10.1109/TEVC.2020.3017865.

handle.net

hdl.handle.net

Rayas-Sanchez, Jose E. (2016). "Power in Simplicity with ASM: Tracing the Aggressive Space Mapping Algorithm over Two Decades of Development and Engineering Applications". IEEE Microwave Magazine. 17 (4): 64–76. doi:10.1109/MMM.2015.2514188. hdl:11117/5948.

harvard.edu

ui.adsabs.harvard.edu

Bandler, J.W.; Cheng, Q.S.; Dakroury, S.A.; Mohamed, A.S.; Bakr, M.H.; Madsen, K.; Sondergaard, J. (2004). "Space Mapping: The State of the Art". IEEE Transactions on Microwave Theory and Techniques. 52 (1): 337–361. Bibcode:2004ITMTT..52..337B. doi:10.1109/TMTT.2003.820904.
Cardenas, IC (2019). "On the use of Bayesian networks as a meta-modeling approach to analyse uncertainties in slope stability analysis". Georisk: Assessment and Management of Risk for Engineered Systems and Geohazards. 13 (1): 53–65. Bibcode:2019GAMRE..13...53C. doi:10.1080/17499518.2018.1498524. S2CID 216590427.
Manzoni, Luca; Papetti, Daniele M.; Cazzaniga, Paolo; Spolaor, Simone; Mauri, Giancarlo; Besozzi, Daniela; Nobile, Marco S. (2020). "Surfing on Fitness Landscapes: A Boost on Optimization by Fourier Surrogate Modeling". Entropy. 22 (3): 285. Bibcode:2020Entrp..22..285M. doi:10.3390/e22030285. PMC 7516743. PMID 33286059.

ieee.org

ieeexplore.ieee.org

Bandler, J.W.; Cheng, Q.S.; Dakroury, S.A.; Mohamed, A.S.; Bakr, M.H.; Madsen, K.; Sondergaard, J. (2004). "Space Mapping: The State of the Art". IEEE Transactions on Microwave Theory and Techniques. 52 (1): 337–361. Bibcode:2004ITMTT..52..337B. doi:10.1109/TMTT.2003.820904.
Rayas-Sanchez, Jose E. (2016). "Power in Simplicity with ASM: Tracing the Aggressive Space Mapping Algorithm over Two Decades of Development and Engineering Applications". IEEE Microwave Magazine. 17 (4): 64–76. doi:10.1109/MMM.2015.2514188. hdl:11117/5948.
Wei, F-F.; Chen, W-N.; Yang, Q.; Deng, J.D.; Luo, X.; Jin, H.; Zhang, J. (2021). "A Classifier-Assisted Level-Based Learning Swarm Optimizer for Expensive Optimization". IEEE Transactions on Evolutionary Computation. 25 (2): 219–233. doi:10.1109/TEVC.2020.3017865.

inria.fr

hal.inria.fr

Loshchilov, I.; M. Schoenauer; M. Sebag (2010). "Comparison-Based Optimizers Need Comparison-Based Surrogates" (PDF). Parallel Problem Solving from Nature (PPSN XI). Springer. pp. 364–1373.

nih.gov

pubmed.ncbi.nlm.nih.gov

Manzoni, Luca; Papetti, Daniele M.; Cazzaniga, Paolo; Spolaor, Simone; Mauri, Giancarlo; Besozzi, Daniela; Nobile, Marco S. (2020). "Surfing on Fitness Landscapes: A Boost on Optimization by Fourier Surrogate Modeling". Entropy. 22 (3): 285. Bibcode:2020Entrp..22..285M. doi:10.3390/e22030285. PMC 7516743. PMID 33286059.
Bliek, Laurens; Verstraete, Hans R. G. W.; Verhaegen, Michel; Wahls, Sander (2018). "Online Optimization with Costly and Noisy Measurements Using Random Fourier Expansions". IEEE Transactions on Neural Networks and Learning Systems. 29 (1): 167–182. arXiv:1603.09620. doi:10.1109/TNNLS.2016.2615134. PMID 27831891.

ncbi.nlm.nih.gov

Manzoni, Luca; Papetti, Daniele M.; Cazzaniga, Paolo; Spolaor, Simone; Mauri, Giancarlo; Besozzi, Daniela; Nobile, Marco S. (2020). "Surfing on Fitness Landscapes: A Boost on Optimization by Fourier Surrogate Modeling". Entropy. 22 (3): 285. Bibcode:2020Entrp..22..285M. doi:10.3390/e22030285. PMC 7516743. PMID 33286059.

ressources-actuarielles.net

Jones, D. R. (2001). "A taxonomy of global optimization methods based on response surfaces" (PDF). Journal of Global Optimization. 21 (4): 345–383. doi:10.1023/A:1012771025575.

sciencedirect.com

Jin, Y-C. (2011). "Surrogate-assisted evolutionary computation: Recent advances and future challenges". Swarm and Evolutionary Computation. 1 (2): 61–70. doi:10.1016/j.swevo.2011.05.001.

semanticscholar.org

api.semanticscholar.org

Cardenas, IC (2019). "On the use of Bayesian networks as a meta-modeling approach to analyse uncertainties in slope stability analysis". Georisk: Assessment and Management of Risk for Engineered Systems and Geohazards. 13 (1): 53–65. Bibcode:2019GAMRE..13...53C. doi:10.1080/17499518.2018.1498524. S2CID 216590427.
Bouhlel, M.A.; Hwang, J.H.; Bartoli, Nathalie; Lafage, R.; Morlier, J.; Martins, J.R.R.A. (2019). "A Python surrogate modeling framework with derivatives". Advances in Engineering Software. 135: 102662. doi:10.1016/j.advengsoft.2019.03.005. S2CID 128324330.

springerprofessional.de

Dasari, S.K.; P. Andersson; A. Cheddad (2019). "Random Forest Surrogate Models to Support Design Space Exploration in Aerospace Use-Case". Artificial Intelligence Applications and Innovations (AIAI 2019). Springer. pp. 532–544. Retrieved 2019-06-02.

umich.edu

mdolab.engin.umich.edu

Bouhlel, M.A.; Hwang, J.H.; Bartoli, Nathalie; Lafage, R.; Morlier, J.; Martins, J.R.R.A. (2019). "A Python surrogate modeling framework with derivatives". Advances in Engineering Software. 135: 102662. doi:10.1016/j.advengsoft.2019.03.005. S2CID 128324330.

worldcat.org

search.worldcat.org

Ranftl, Sascha; von der Linden, Wolfgang (2021-11-13). "Bayesian Surrogate Analysis and Uncertainty Propagation". Physical Sciences Forum. 3 (1): 6. arXiv:2101.04038. doi:10.3390/psf2021003006. ISSN 2673-9984.

zenodo.org

Kernc (2024), SAMBO: Sequential And Model-Based Optimization: Efficient global optimization in Python, doi:10.5281/zenodo.14461363