TorchOptics: An open-source Python library for differentiable Fourier optics simulations
Fuente:
arXiv
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| Autori principali: | , |
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| Natura: | Preprint |
| Pubblicazione: |
2024
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| _version_ | 1866916497509580800 |
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| author | Filipovich, Matthew J. Lvovsky, A. I. |
| author_facet | Filipovich, Matthew J. Lvovsky, A. I. |
| contents | TorchOptics is an open-source Python library for differentiable Fourier optics simulations, developed using PyTorch to enable GPU-accelerated tensor computations and automatic differentiation. It provides a comprehensive framework for modeling, analyzing, and designing optical systems using Fourier optics, with applications in imaging, diffraction, holography, and signal processing. The library leverages PyTorch's automatic differentiation engine for gradient-based optimization, enabling the inverse design of complex optical systems. TorchOptics supports end-to-end optimization of hybrid models that integrate optical systems with machine learning architectures for digital post-processing. The library includes a wide range of optical elements and spatial profiles, and supports simulations with polarized light and fields with arbitrary spatial coherence. |
| format | Preprint |
| id |
arxiv_https___arxiv_org_abs_2411_18591 |
| institution | arXiv |
| publishDate | 2024 |
| record_format | arxiv |
| spellingShingle | TorchOptics: An open-source Python library for differentiable Fourier optics simulations Filipovich, Matthew J. Lvovsky, A. I. Optics Classical Physics Computational Physics TorchOptics is an open-source Python library for differentiable Fourier optics simulations, developed using PyTorch to enable GPU-accelerated tensor computations and automatic differentiation. It provides a comprehensive framework for modeling, analyzing, and designing optical systems using Fourier optics, with applications in imaging, diffraction, holography, and signal processing. The library leverages PyTorch's automatic differentiation engine for gradient-based optimization, enabling the inverse design of complex optical systems. TorchOptics supports end-to-end optimization of hybrid models that integrate optical systems with machine learning architectures for digital post-processing. The library includes a wide range of optical elements and spatial profiles, and supports simulations with polarized light and fields with arbitrary spatial coherence. |
| title | TorchOptics: An open-source Python library for differentiable Fourier optics simulations |
| topic | Optics Classical Physics Computational Physics |
| url | https://arxiv.org/abs/2411.18591 |