Optical neural engine for solving scientific partial differential equations
Abstract Solving partial differential equations (PDEs) is the cornerstone of scientific research and development. Data-driven machine learning (ML) approaches are emerging to accelerate time-consuming and computation-intensive numerical simulations of PDEs. Although optical systems offer high-throug...
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| Main Authors: | , , , , , , , |
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| Format: | Article |
| Language: | English |
| Published: |
Nature Portfolio
2025-05-01
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| Series: | Nature Communications |
| Online Access: | https://doi.org/10.1038/s41467-025-59847-3 |
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