Reference number : 172-2021-2022-Q4-987397, amendment number : 1, agreement type : G, recipient legal name : Université de Montréal, recipient country : CA, recipient province : QC, recipient city : Montreal|Montréal, recipient postal code : H3T 1J4, program name english : Collaborative Science, Technology and Innovation Program - Collaborative R&D Initiatives, program name french : Programme de collaboration en science, en technologie et en innovation - Initiatives de collaboration en R-D, program purpose english : Collaborate on multiparty research and development programs to catalyze transformative, high-risk, high-reward research with the potential for game-changing scientific discoveries and technological breakthroughs in priority areas., program purpose french : Collaborer à des programmes multipartites de recherche-développement dans le but de profiter de la synergie entre la recherche transformatrice à haut risque et à haut rendement et le potentiel de découvertes scientifiques et de percées technologiques révolutionnaires dans des secteurs d’intervention prioritaires., agreement title english : Generative models for integrated photonic components – design and implementation of a new generator-discriminator approach, agreement title french : Modèles génératifs pour les composants photoniques intégrés — conception et mise en œuvre d’une nouvelle approche générateur-discriminateur, agreement number : 987397, agreement value : 159500.0, agreement start date : 2022-03-21, agreement end date : 2025-09-30, description english : This Project will focus on adapting a general learning framework to improvements related to nanophotonic component design. Novel optimization techniques such as inverse design are promising tools to significantly reduce the size of nanophotonic components while maintaining their functionality and performance. Although published results demonstrate various proof of concept miniaturized devices with pre-determined size and aspect ratio, their performances as of now are inferior to the state of the art classical devices which are however much larger, with designs that are highly non-interpretable.  In this respect, recent developments in learning generative models through the use of generative-adversarial architecture is a promising technological breakthrough. Specifically, this type of training provides a framework to build neural-network based models capable of generating a diverse set of useful data instances in high-dimensional spaces, which are well-performing designs in this context, description french : Modèles génératifs pour les composants photoniques intégrés — conception et mise en œuvre d’une nouvelle approche générateur-discriminateur, expected results english : In the short term, anticipated outcomes will be strengthened collaborations across industry, academia, and government to support research excellence. In the medium term, anticipated outcomes will be the development of new and potentially disruptive technologies with collaborators. In the long term, find collaborative solutions to public policy challenges and create stronger innovation systems., expected results french : À court terme, les résultats prévus seront des collaborations renforcées entre l'industrie, le milieu universitaire et le gouvernement pour soutenir l'excellence en recherche. À moyen terme, les résultats attendus seront le développement de nouvelles technologies potentiellement perturbatrices avec des collaborateurs. À long terme, trouver des solutions collaboratives aux défis des politiques publiques et créer des systèmes d'innovation plus solides., additional information english : This agreement has been amended 1 time(s). The end date of this agreement has been modified by 547 days., additional information french : Cet accord a été modifié 1 fois.  La date de fin du présent accord a été changé de 547 jours., owner organization : nrc-cnrc, owner organization title : National Research Council Canada | Conseil national de recherches Canada