Machine learning techniques to solve optical design challenges
Data-driven approaches – propelled by the widespread success of machine learning and especially deep learning – have the potential to revolutionize optical design.
Optical system design is a large nonlinear optimization problem that is usually addressed using traditional local and global optimization methods. Traditional methods do not learn from data, and therefore must start from scratch for any new design process. A novel family of data-driven approaches, relatively unexplored, offer the ability to extract the features that make up a good design and reuse them to infer new designs – similar to how optical design practitioners develop and then exploit their intuition and knowledge.
Our research group, a pioneer in this field, wants to further develop a deep learning approach that extrapolates from previous designs to directly infer optical systems tailored to the desired specifications. Other approaches such as training an optimization algorithm may also be explored.
- Lens design
- Artificial intelligence
- Machine learning
Optical Engineering research lab
Profs. Simon Thibault and Jean-François Lalonde have been working together for a few years now. Their efforts focus on how machine learning techniques can be applied to solve challenges that optical designers face when designing a new optical system. Their expertise in optics and machine learning are highly complementary and their work has received attention from the international community. In addition, Profs. Thibault and Lalonde's research groups are known for their dynamism and the emphasis on teamwork.
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Master's Degree in Physics with thesis
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Professionnelle de recherche
Département de physique, de génie physique et d'optique