Neural networks with change-of-variables and mesh-based losses outperform a deconvolution baseline in accuracy and speed for 2D finite-source reflector design on four benchmarks.
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Neural-network methods for two-dimensional finite-source reflector design
Neural networks with change-of-variables and mesh-based losses outperform a deconvolution baseline in accuracy and speed for 2D finite-source reflector design on four benchmarks.