[pre-ANN] Differentiable FDTD for inverse design in photonics, acoustics and RF

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Beautiful. :slight_smile: Works perfectly. I ran a check with 2D on the first one for speed / I’ll run tests on the other case later, but overall, excellent work!

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Thanks for being an early tester! We ironed out issues and released a new notebook with examples. If you’re benchmarking speed I’ll need to separate out the compilation part (todo list). Here are some inverse designs of wavelength domain demultiplexer and symmetric crossing, all in just few lines of code.

download (2)
download (2)750×562 61.4 KB

download (1)
download (1)1200×900 75.5 KB

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We compiled Julia FDTD backend to binary with PackageCompiler.jl so it’s much faster now. New notebook w/ installation, sparams and inverse design at Google Colab

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We implemented tensor subpixel smoothing for accuracy and adaptive grid for speed. Please contact us for latest installation binaries. New notebook Google Colab

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