PERSONAL Sign in with your SPIE account to access your personal subscriptions or to use specific features such as save to my library, sign up for alerts, save searches, etc.
Machine learning is widely used for optimization or classification tasks. Unfortunately, extensive labeled datasets are often required for training machine learning models. In this work we demonstrate that incorporating physics-driven constraints into machine learning algorithms can dramatically improve both accuracy and extendibility of resulting models, simultaneously reducing the size of the required training set and enabling training on unlabeled data. Physics-informed machine learning is illustrated on example of predicting optical modes supported by periodic layered composites. The approach can be readily utilized for analysis of electromagnetic modes in composites with 2D periodic geometry or in complex waveguiding structures.
PERSONAL Sign in with your SPIE account to access your personal subscriptions or to use specific features such as save to my library, sign up for alerts, save searches, etc.
The alert did not successfully save. Please try again later.
Viktor A. Podolskiy, Abantika Ghosh, Mohannad Elhamod, Jie Bu, Wei-Cheng Lee, Anuj Karpatne, "Science-informed machine-learning for optical composites (Conference Presentation)," Proc. SPIE PC12195, Metamaterials, Metadevices, and Metasystems 2022, PC121950Y (3 October 2022); https://doi.org/10.1117/12.2632454