April 30, 2023 at 7:49 amMd Dulal HaqueSubscriber
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.
May 1, 2023 at 9:26 pmGuilin SunAnsys Employee
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.
May 2, 2023 at 6:10 amMd Dulal HaqueSubscriber
Thank you very much for your comments
May 13, 2023 at 5:56 amYao XiaoSubscriber
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
May 15, 2023 at 3:25 amMd Dulal HaqueSubscriber
May 15, 2023 at 3:28 amMd Dulal HaqueSubscriber
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 firstname.lastname@example.org
May 15, 2023 at 3:08 pmGuilin SunAnsys Employee
You can discuss in FEED by inviting the other party and friends.
May 16, 2023 at 1:43 amMd Dulal HaqueSubscriber
Dear Guilin Sun,
Thank you very much for arranging the discussion. Please let me know the discussion schedule in advance.
May 16, 2023 at 2:27 amMd Dulal HaqueSubscriber
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.
May 16, 2023 at 4:42 pmGuilin SunAnsys Employee
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.
May 24, 2023 at 8:16 amJoe SuarezSubscriber
- 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.
- 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.
- 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.
- 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.
- 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.
- 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.
- 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.
- 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.
- 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.
- 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.
May 24, 2023 at 2:39 pmMd Dulal HaqueSubscriber
Dear Joe Suarez,
Thank you very much for your explanation.
May 24, 2023 at 6:20 pmGuilin SunAnsys Employee
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.
May 26, 2023 at 10:15 pmGuilin SunAnsys Employee
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
May 27, 2023 at 5:11 amMd Dulal HaqueSubscriber
Dear Guilin Sun,
Thank you very much for your sharing the resources related to the deep learining for metasurfaces.
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