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Advances in surrogate based modeling, feasibility analysis, and optimization: A review
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Bio-inspired computation: Where we stand and what's next
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Osaba, E., Villar-Rodriguez, E., Del Ser, J., Nebro, A. J., Molina, D., LaTorre, A., Suganthan, P. N., Coello Coello, C. A., & Herrera, F. (2021) · Swarm and Evolutionary Computation · doi:10.1016/j.swevo.2021.100888 · Cited by 406
Overview of Surrogate Modeling in Chemical Process Engineering
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Perspectives on the integration between first-principles and data-driven modeling
Bradley, W., Kim, J., Kilwein, Z., Blakely, L., Eydenberg, M., Jalvin, J., Laird, C., & Boukouvala, F. (2022) · Computers & Chemical Engineering · doi:10.1016/j.compchemeng.2022.107898 · Cited by 231
Metadata
- Authors
- Bhosekar, A., & Ierapetritou, M.
- Venue
- Computers & Chemical Engineering
- Publisher
- Elsevier BV
- Published
- 2018-01
- Volume
- 108
- Pages
- 250-267
- ISSN
- 0098-1354 (ISSN-L)
- OpenAlex
- W2756659234
- Cited by
- 707
- References
- 121
- FWCI
- 31.31
- Type
- journal article
- Language
- en
- Open access
- closed
- Licence
- www.elsevier.com/tdm/userlicense/1.0/ (tdm)
- Funders
- NSF (1159244, 1434548)
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- Bhosekar, A., & Ierapetritou, M. (2018). Advances in surrogate based modeling, feasibility analysis, and optimization: A review. Computers & Chemical Engineering. https://doi.org/10.1016/j.compchemeng.2017.09.017
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Contains information from the Semantic Scholar Academic Graph Datasets, made available under the ODC Attribution License (ODC-BY 1.0).
Semantic Scholar’s data is described in: Kinney, R., et al. (2023). The Semantic Scholar Open Data Platform. arXiv:2301.10140.
