10 citations · 10 across the 2 of their papers we have counts for
3 papers
Physics-Informed Machine Learning for Optical Modes in Composites
Abantika Ghosh, Mohannad Elhamod, Jie Bu +3
We demonstrate that embedding physics-driven constraints into machine learning process can dramatically improve accuracy and generalizability of the resulting model. Physics-inform…
CoPhy-PGNN: Learning Physics-guided Neural Networks with Competing Loss Functions for Solving Eigenvalue Problems
Mohannad Elhamod, Jie Bu, Christopher Singh +5
Physics-guided Neural Networks (PGNNs) represent an emerging class of neural networks that are trained using physics-guided (PG) loss functions (capturing violations in network out…
Machine learning -- based diffractive imaging with subwavelength resolution
Abantika Ghosh, Diane J. Roth, Luke H. Nicholls +3
Far-field characterization of small objects is severely constrained by the diffraction limit. Existing tools achieving sub-diffraction resolution often utilize point-by-point image…