Learning Forum

[Md Dulal Haque]

Dear Researchers,

I do not understand how to write the script of the data generation from FDTD lumerical  for the deep learning model of  metasurface and metamaterials. If anyone know about it please help me to write the scripts for the data generation by FDTD  and implement in deep learing model for metasurface and metamaterails.

Thank you very much in advanced for kind cooperation.

[Guilin Sun]

This is a new topic and we have no similar example.

You may use sweep to get some data of the phase change vs geometry and polarization for given material and periodicity. Using those data to train the AI. later when you specify the phase desired it may give you possible geometry and periodicity.

please refer the sweep: https://optics.ansys.com/hc/en-us/articles/360034922873-Parameter-sweep-utility

then using script getsweepresult, and reognaze the data to feed the deep leaning.

[Md Dulal Haque]

Thank you very much for your comments

[Yao Xiao]

Dear friend, i have the same idea and interesting to want to combine the fdtd and machine learning to develop our design. so can i keep communicating with you

[Md Dulal Haque]

[Md Dulal Haque]

Dear Yao Xiao, Thank you very much for your interest. I will be very much happy, If you communicate with regarding this  issue (the fdtd and machine learning to develop our design) My Email address is dhaque@hstu.ac.bd

[Guilin Sun]

You can discuss in FEED by inviting the other party and friends.

[Md Dulal Haque]

Dear Guilin Sun,

Thank you very much for arranging the discussion. Please let me know the discussion schedule in advance. 

[Md Dulal Haque]

Dear Guilin Sun,

Coul you kindly share me a script or link  for exporting sweep data from FDTD lumerical  for savin in directory  in CSV/Xcel format.

Thank you very much for your kind cooperation in advance.

[Guilin Sun]

Currently Lumerical can read excel data. Saving data in txt, ldf and matlab is available
txt: use write

ldf: use savedata

matlab: use matlabsave

This is different topic from the original post. Later please write a new post for any new questions.

 

[Joe Suarez]

1. Launch Lumerical FDTD Solutions and create a new project. Define the simulation parameters, such as the size of the computational domain, material properties, source parameters, and boundary conditions. These parameters will depend on the specific problem you are solving.
2. Use Lumerical’s layout tools to create the desired metasurface or metamaterial structure. This may involve defining the geometry, material properties, and arrangement of unit cells.
3. Once you have set up the simulation, run the FDTD simulation to obtain the electromagnetic field distribution and other desired outputs. Lumerical will perform the necessary calculations to solve Maxwell’s equations in the defined domain.
4. After the simulation completes, you can extract data at specific locations or surfaces of interest. This may include field profiles, transmission/reflection coefficients, near-field distributions, or any other relevant information you need for your deep-learning model.
5. To generate a diverse dataset, you can repeat steps 3-5 for various parameter configurations, such as changing the geometrical parameters, material properties, incident angles, or polarization states.
6. Save the extracted data in a suitable format that is compatible with your deep learning framework. Common formats include CSV, HDF5, or specialized file formats specific to deep learning libraries.
7. Use a deep learning framework of your choice (e.g., TensorFlow, PyTorch, Keras) to develop a neural network model for metasurface or metamaterial characterization. The specifics of implementing the model will depend on the architecture and objective of your deep learning model.
8. Before feeding the data into the deep learning model, you may need to preprocess it. This could involve normalization, resizing, or augmenting the data to enhance the model's performance.
9. Split your dataset into training, validation, and test sets. Use the training set to train the deep learning model and adjust its weights and biases based on the data. Evaluate the model's performance using the validation set and make necessary adjustments to improve it.
10. Finally, evaluate the trained model's performance using the test set, which contains unseen data. Analyze the model's predictions and assess its lead data enrichment accuracy and generalization capabilities.

 

[Md Dulal Haque]

Dear Joe Suarez, 

Thank you very much for your explanation.

 

[Guilin Sun]

Thank you Joe for the above suggestions!

Deep learning is the current trend, and many people are interested in this, including us Lumerical. But currently we do not have the resources for such work yet.

[Guilin Sun]

I found this issue of EEE Journal on Multiscale and Multiphysics Computational Techniques has a few papers related to deep learning for metasurface and others

https://ieeexplore.ieee.org/xpl/mostRecentIssue.jsp?punumber=7274859&mkt_tok=NzU2LUdQSC04OTkAAAGL83m7-R7cd_O-4uKtlXZyUGHXyQjfPPparF53U7Rxnz96i8AktEYaTCezc0WOkAM0tYTuYogk3Ye5oTdsK9eo10aHPx9YCynbVjvPb8-Z5rg

[Md Dulal Haque]

Dear Guilin Sun,

Thank you very much for your sharing the resources related to the deep learining  for metasurfaces.