We demonstrate that incorporating physics-based intuition and Maxwell-equation-based constraints into machine learning process reduces the required amount of the training data and improves prediction accuracy and physics consistency. In addition, physics-based provides an avenue to extend the range of the model applicability outside the space of the original labeled dataset. The proposed approaches are illustrated on examples of photonic composites, from photonic crystals to hyperbolic metamaterials.
Access to the requested content is limited to institutions that have purchased or subscribe to SPIE eBooks.
You are receiving this notice because your organization may not have SPIE eBooks access.*
*Shibboleth/Open Athens users─please
sign in
to access your institution's subscriptions.
To obtain this item, you may purchase the complete book in print or electronic format on
SPIE.org.
INSTITUTIONAL Select your institution to access the SPIE Digital Library.
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.