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Collaboration networkThis graph maps the connections between all the collaborators of {}'s publications listed on this page.
Each link represents a collaboration on the same publication. The thickness of the link represents the number of collaborations.
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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.
Gamache, J.-F., Vadean, A., Capo, M., Rochefort-Beaudoin, T., Dodane, N., & Achiche, S. (2023). Complexity-driven layout exploration for aircraft structures. Design Science, 9, 34 pages. External link
Rochefort-Beaudoin, T., Vadean, A., Aage, N., & Achiche, S. (2025). Structural design through reinforcement learning. Engineering Applications of Artificial Intelligence, 161, 112273 (13 pages). External link
Rochefort-Beaudoin, T., Vadean, A., Achiche, S., & Aage, N. (2025). From density to geometry: instance segmentation for reverse engineering of optimized structures. Engineering Applications of Artificial Intelligence, 141, 109732 (20 pages). Available
Rochefort-Beaudoin, T. (2024). A Novel Framework for Accelerating Topology Optimization : Merging Machine Learning with Feature-Mapping Methods [Ph.D. thesis, Polytechnique Montréal]. Available
Rochefort-Beaudoin, T., Vadean, A., Gamache, J.-F., & Achiche, S. (2023). Supervised deep learning for the moving morphable components topology optimization framework. Engineering Applications of Artificial Intelligence, 123, 15 pages. External link
Rochefort-Beaudoin, T., Vadean, A., Gamache, J.-F., & Achiche, S. (2022, August). Comparative study of first-order moving asymptotes optimizers for the moving morphable components topology optimization framework [Paper]. ASME International Design Engineering Technical Conferences and Computers and Information in Engineering Conference (DETC 2022), St. Louis, MO, USA. External link
