Amin Heyrani Nobari
Amin Heyrani Nobari is a PhD student whose research explores mechanical design automation and applied machine learning (ML) for mechanical design, with the goal of making major advancements in engineering design and product development. His current work focuses on automating CAD generation using sequential ML models and large language models, introducing ML models for expediting or replacing high-fidelity physics simulations, and designing under constraints using ML. With the support of his third MathWorks Fellowship, Amin will expand ongoing work to create generative models for inverse kinematic design of linkage mechanisms, which involves developing methods that allow algorithms to suggest 2D planar linkage mechanism designs for any specified target. Furthermore, he will be working on developing generative optimization methods combining deep generative models and optimization methods in engineering design problems. To facilitate his work on generative ML models for linkage synthesis, Amin has created an algorithm that simulates mechanisms 800 times faster than previous methods. His work, which makes extensive use of MathWorks tools, has resulted in several important contributions to computational generative design and has great potential to drive future innovations in the engineering field.