Edoardo Vergerio 5f88780426 modified mpid to use filtering on the phase mask parameters. Automatic differentiation is used to correctly compute gradients. Moreover, the optimization can down be carried out according to different strategies, which can either be: average the objectives, use an epigraph reformulation. The epigraph formulation needs further attention because it does not always leed to good results. A possible solution might be a combination of the two strategies: at the beginning use an average cost function to set up the problem and then switch to an epigraph. This would be like an hot start strategy, since I have noticed the epigraph being not totally capable of handling the problem well if it starts from scratch. On the other hand, the use of the filtering action can even leed to better results than without it, although it is necessary to tune the filter radius. I have seen that even a 1um radius is generally sufficient to remove outliers in the final phase mask, which was the main reason we decided to implement the filter in the first place. In the end, the all defined classes assume we interface to them using dimensions in micrometers. Conversion to meters is handled internally.

2026-09-11 14:45:35 +02:00

filters.py

implemented and did some testing about applying a conic filter on phase masks. This should smooth out outliers at the cost of a possible reduction of total overlap efficiency. Several example devices have been added for testing purposes.

2026-09-08 16:56:56 +02:00

mpid.py

modified mpid to use filtering on the phase mask parameters. Automatic differentiation is used to correctly compute gradients. Moreover, the optimization can down be carried out according to different strategies, which can either be: average the objectives, use an epigraph reformulation. The epigraph formulation needs further attention because it does not always leed to good results. A possible solution might be a combination of the two strategies: at the beginning use an average cost function to set up the problem and then switch to an epigraph. This would be like an hot start strategy, since I have noticed the epigraph being not totally capable of handling the problem well if it starts from scratch. On the other hand, the use of the filtering action can even leed to better results than without it, although it is necessary to tune the filter radius. I have seen that even a 1um radius is generally sufficient to remove outliers in the final phase mask, which was the main reason we decided to implement the filter in the first place. In the end, the all defined classes assume we interface to them using dimensions in micrometers. Conversion to meters is handled internally.

2026-09-11 14:45:35 +02:00

single_converter_symm.py

implemented and did some testing about applying a conic filter on phase masks. This should smooth out outliers at the cost of a possible reduction of total overlap efficiency. Several example devices have been added for testing purposes.

2026-09-08 16:56:56 +02:00

sorter_to_gauss.py

modified mpid to use filtering on the phase mask parameters. Automatic differentiation is used to correctly compute gradients. Moreover, the optimization can down be carried out according to different strategies, which can either be: average the objectives, use an epigraph reformulation. The epigraph formulation needs further attention because it does not always leed to good results. A possible solution might be a combination of the two strategies: at the beginning use an average cost function to set up the problem and then switch to an epigraph. This would be like an hot start strategy, since I have noticed the epigraph being not totally capable of handling the problem well if it starts from scratch. On the other hand, the use of the filtering action can even leed to better results than without it, although it is necessary to tune the filter radius. I have seen that even a 1um radius is generally sufficient to remove outliers in the final phase mask, which was the main reason we decided to implement the filter in the first place. In the end, the all defined classes assume we interface to them using dimensions in micrometers. Conversion to meters is handled internally.

2026-09-11 14:45:35 +02:00

sorter_to_ident.py

implemented and did some testing about applying a conic filter on phase masks. This should smooth out outliers at the cost of a possible reduction of total overlap efficiency. Several example devices have been added for testing purposes.

2026-09-08 16:56:56 +02:00

testing_mode_sorter.py

scripts for testing usage of the mpid class. Shaping the inverse design algorithm into a class has proven useful to streamline the design process. Being more easy to handle, one can now test various devices with very little setup effort.

2026-09-07 13:42:16 +02:00

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