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Word cloudA word cloud is a visual representation of the most frequently used words in a text or a set of texts. The words appear in different sizes, with the size of each word being proportional to its frequency of occurrence in the text. The more frequently a word is used, the larger it appears in the word cloud. This technique allows for a quick visualization of the most important themes and concepts in a text.
In the context of this page, the word cloud was generated from the publications of the author {}. The words in this cloud come from the titles, abstracts, and keywords of the author's articles and research papers. By analyzing this word cloud, you can get an overview of the most recurring and significant topics and research areas in the author's work.
The word cloud is a useful tool for identifying trends and main themes in a corpus of texts, thus facilitating the understanding and analysis of content in a visual and intuitive way.
Audet, C., Kokkolaras, M., Le Digabel, S., & Talgorn, B. (2018). Order-based error for managing ensembles of surrogates in derivative-free optimization. (Technical Report n° G-2016-36). External link
Audet, C., Kokkolaras, M., Le Digabel, S., & Talgorn, B. (2018). Order-based error for managing ensembles of surrogates in mesh adaptive direct search. Journal of Global Optimization, 70(3), 645-675. External link
Pourbagian, M., Talgorn, B., Habashi, W. G., Kokkolaras, M., & Le Digabel, S. (2015). Constrained problem formulations for power optimization of aircraft electro-thermal anti-icing systems. Optimization and Engineering, 16(4), 663-693. External link
Pourbagian, M., Talgorn, B., Habashi, W. G., Kokkolaras, M., & Le Digabel, S. (2014). On power optimization of aircraft electro-thermal anti-icing systems. (Technical Report n° G-2014-72). External link
Talgorn, B., Audet, C., Le Digabel, S., & Kokkolaras, M. (2018). Locally weighted regression models for surrogate-assisted design optimization. Optimization and Engineering, 19(1), 213-238. External link
Talgorn, B., Audet, C., Le Digabel, S., & Kokkolaras, M. (2016). Locally weighted regression models for surrogate-assisted design optimization. (Technical Report n° G-2016-113). External link
Talgorn, B., Le Digabel, S., & Kokkolaras, M. (2015). Blackbox Optimization in Engineering Design: Adaptive Statistical Surrogates and Direct Search Algorithms. In Lagaros, N. D., & Papadrakakis, M. (eds.), Engineering and Applied Sciences Optimization (pp. 359-383). External link
Talgorn, B., Le Digabel, S., & Kokkolaras, M. (2015). Statistical Surrogate Formulations for Simulation-Based Design Optimization. Journal of Mechanical Design, 137(2), 021405. External link
Talgorn, B., Le Digabel, S., & Kokkolaras, M. (2014). Problem formulations for simulation-based design optimization using statistical surrogates and direct search. (Technical Report n° G-2014-04). External link
Talgorn, B., Le Digabel, S., & Kokkolaras, M. (2014, August). Problem Formulations for Simulation-Based Design Optimization Using Statistical Surrogates and Direct Search [Paper]. ASME 2014 International Design Engineering Technical Conferences & Computers and Information in Engineering Conference (IDETC/CIE 2014), Buffalo, New York. External link
