NanoPhotoNet-Lase PINN embeds Maxwell's equations and four-level lasing dynamics to predict metasurface nanolaser thresholds, experimentally validated with <1% deviation and used to realize coherent beam-steering metalenses.
Exploring AI in metasurface structures with forward and inverse design
2 Pith papers cite this work. Polarity classification is still indexing.
2
Pith papers citing it
fields
physics.optics 2years
2026 2verdicts
UNVERDICTED 2representative citing papers
A review assessing PINN advances for forward modeling, inverse design, and equation discovery across multi-physics domains.
citing papers explorer
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On-Demand Coherent Nanolaser Metalens and Beam Steering Enabled by Physics-Informed Neural Networks
NanoPhotoNet-Lase PINN embeds Maxwell's equations and four-level lasing dynamics to predict metasurface nanolaser thresholds, experimentally validated with <1% deviation and used to realize coherent beam-steering metalenses.
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Beyond Data-Driven: How Physics-Informed Neural Networks are Reshaping Multi-Physics Design and Discovery
A review assessing PINN advances for forward modeling, inverse design, and equation discovery across multi-physics domains.