Dataset supports: Wiecha, P. R. & Muskens, O. L. "Deep learning meets nanophotonics: A generalized accurate predictor for near fields and far fields of arbitrary 3D nanostructures". Nano Letters (2019) Simulation data and analysis</span
Precise control over dimension of nanocrystals is critical to tune the properties for various applic...
Dataset supports: Ou, J., Plum, E., Zheludev, N. (2018). Optical addressing of nanomechanical metam...
Dataset supports: Ou, J., Plum, E., Zheludev, N. (2018). Optical addressing of nanomechanical metam...
Deep artificial neural networks are powerful tools with many possible applications in nanophotonics....
Three different deep learning models were designed in this paper, to predict the electric fields of ...
A central challenge in contemporary materials and photonics research is understanding how intrinsic ...
A central challenge in contemporary materials and photonics research is understanding how intrinsic ...
Raw data for numerical simulation results that support the paper "Deep learning enabled strate...
Deep learning has become the dominant approach in artificial intelligence to solve complex data-driv...
This dataset contains: Figures, in the form of vector files (containing data) and PNG files</span
International audienceSubwavelength small particles can be tailored to fulfill manifold functionalit...
Challenging interdisciplinary applications inspire new methodological developments in data understan...
© 2019 American Chemical Society. Deep learning is known to be data-hungry, which hinders its applic...
This thesis discusses the application of deep learning to Coherent FourierScatterometry data in orde...
Scanning near-field optical microscopy is one of the most effective techniques for spectroscopy of n...
Precise control over dimension of nanocrystals is critical to tune the properties for various applic...
Dataset supports: Ou, J., Plum, E., Zheludev, N. (2018). Optical addressing of nanomechanical metam...
Dataset supports: Ou, J., Plum, E., Zheludev, N. (2018). Optical addressing of nanomechanical metam...
Deep artificial neural networks are powerful tools with many possible applications in nanophotonics....
Three different deep learning models were designed in this paper, to predict the electric fields of ...
A central challenge in contemporary materials and photonics research is understanding how intrinsic ...
A central challenge in contemporary materials and photonics research is understanding how intrinsic ...
Raw data for numerical simulation results that support the paper "Deep learning enabled strate...
Deep learning has become the dominant approach in artificial intelligence to solve complex data-driv...
This dataset contains: Figures, in the form of vector files (containing data) and PNG files</span
International audienceSubwavelength small particles can be tailored to fulfill manifold functionalit...
Challenging interdisciplinary applications inspire new methodological developments in data understan...
© 2019 American Chemical Society. Deep learning is known to be data-hungry, which hinders its applic...
This thesis discusses the application of deep learning to Coherent FourierScatterometry data in orde...
Scanning near-field optical microscopy is one of the most effective techniques for spectroscopy of n...
Precise control over dimension of nanocrystals is critical to tune the properties for various applic...
Dataset supports: Ou, J., Plum, E., Zheludev, N. (2018). Optical addressing of nanomechanical metam...
Dataset supports: Ou, J., Plum, E., Zheludev, N. (2018). Optical addressing of nanomechanical metam...
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