Raman Lab @ UCLA

Atmosphere_Model

DeepAdjoint_ML-Optimization_Integration

Here, we combine deep learning (cDCGAN) with adjoint optimization (LumOpt) within a single user-friendly application (DeepAdjoint)). Reference: https://pubs.acs.org/doi/10.1021/acsphotonics.2c00968

Explainability_for_Photonics

Here, we use Deep SHAP (or SHAP) to explain the behavior of nanophotonic structures learned by a convolutional neural network (CNN). Reference: https://pubs.acs.org/doi/full/10.1021/acsphotonics.0c01067

ML_Tutorials

Multiclass_Metasurface_InverseDesign

Here, we use a conditional deep convolutional generative adversarial network (cDCGAN) to inverse design across multiple classes of metasurfaces. Reference: https://onlinelibrary.wiley.com/doi/10.1002/adom.202100548

Reinforcement_Learning_PPO_Metagrating

Scotch_Tape_Radiative_Cooler

Raman-Lab-UCLA/Multiclass_Metasurface_InverseDesign

Here, we use a conditional deep convolutional generative adversarial network (cDCGAN) to inverse design across multiple classes of metasurfaces. Reference: https://onlinelibrary.wiley.com/doi/10.1002/adom.202100548

Raman-Lab-UCLA/Explainability_for_Photonics

Here, we use Deep SHAP (or SHAP) to explain the behavior of nanophotonic structures learned by a convolutional neural network (CNN). Reference: https://pubs.acs.org/doi/full/10.1021/acsphotonics.0c01067

Raman-Lab-UCLA/DeepAdjoint_ML-Optimization_Integration

Here, we combine deep learning (cDCGAN) with adjoint optimization (LumOpt) within a single user-friendly application (DeepAdjoint)). Reference: https://pubs.acs.org/doi/10.1021/acsphotonics.2c00968

Raman-Lab-UCLA/Scotch_Tape_Radiative_Cooler

Raman-Lab-UCLA/ML_Tutorials

Raman-Lab-UCLA/Atmosphere_Model

Raman-Lab-UCLA/Reinforcement_Learning_PPO_Metagrating