494 citations · 1.9k across the 37 of their papers we have counts for
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Deep learning for nano-photonic materials -- The solution to everything!?
Peter R. Wiecha
Deep learning is currently being hyped as an almost magical tool for solving all kinds of difficult problems that computers have not been able to solve in the past. Particularly in…
pyGDM -- new functionalities and major improvements to the python toolkit for nano-optics full-field simulations
Peter R. Wiecha, Clément Majorel, Arnaud Arbouet +4
pyGDM is a python toolkit for electro-dynamical simulations of individual nano-structures, based on the Green Dyadic Method (GDM). pyGDM uses the concept of a generalized propagato…
Deep learning meets nanophotonics: A generalized accurate predictor for near fields and far fields of arbitrary 3D nanostructures
Peter R. Wiecha, Otto L. Muskens
Deep artificial neural networks are powerful tools with many possible applications in nanophotonics. Here, we demonstrate how a deep neural network can be used as a fast, general p…
pyGDM -- A python toolkit for full-field electro-dynamical simulations and evolutionary optimization of nanostructures
Peter R. Wiecha
pyGDM is a python toolkit for electro-dynamical simulations in nano-optics based on the Green Dyadic Method (GDM). In contrast to most other coupled-dipole codes, pyGDM uses a gene…