A physics-based transfer learning scheme improves a neural-network surrogate for quantum cascade laser design, enabling genetic-algorithm optimization about 80,000 times faster than numerical simulation.
Femtosecond luminescence measurements of the intersubband scattering rate in AlxGa1−xAs/GaAs quantum wells under selective excitation,
1 Pith paper cite this work. Polarity classification is still indexing.
1
Pith paper citing it
citation-role summary
background 1
citation-polarity summary
fields
physics.optics 1years
2025 1verdicts
CONDITIONAL 1roles
background 1polarities
unclear 1representative citing papers
citing papers explorer
-
Efficient nanophotonic devices optimization using deep neural network trained with physics-based transfer learning (PBTL) methodology
A physics-based transfer learning scheme improves a neural-network surrogate for quantum cascade laser design, enabling genetic-algorithm optimization about 80,000 times faster than numerical simulation.