arXiv Machine Learning June 3, 2026 By Yitian Zhang, Yonghong chen, Youming Chen, Yiyang Li, Xing Zhe, Renhe Lu, Shaolin Liao, Yuzhe Ma, Zhong Guan

Will Accurate Fields Mislead Photonic Design? FromGlobal Accuracy to Port Readout

Read the original on arXiv Machine Learning →

arXiv:2606. 03038v1 Announce Type: new Abstract: Neural field surrogates can accelerate photonic design loops, but a surrogate that looks accurate in global field error can still mis-rank candidate devices when the final decision depends on localized output-port readouts.

Summary generated by The Flow from the publisher's feed. The full article lives at arXiv Machine Learning.

benchmarks

arXiv Machine Learning

Jul 27

Reliability-Aware Bayesian Optimization of 1310 nm PCSELs with FDTD Verification

arXiv:2607. 21772v1 Announce Type: cross Abstract: Near 1310 nm photonic-crystal surface-emitting lasers (PCSELs) are attractive narrow-beam sources for optical communication and sensing, but their final design refinement is costly.

By Jinglin Yu, Feiyang Wu, Longying Wen, Chongxian Yuan, Renjie Li, Zhaoyu Zhang

arXiv AI

Jul 28

When Every Simulation Counts: Value-Based Reinforcement Learning for Accelerated Photonics Inverse Design

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By Longying Wen, Feiyang Wu, Jinglin Yu, Chongxian Yuan, Renjie Li, Zhaoyu Zhang

reinforcement-learning

arXiv AI

Jul 23

Opto-ViT-v2: Noise-Resilient On-Chip Fine-Tuning for Photonic Near-Sensor Vision Transformer Accelerators

arXiv:2607. 19421v1 Announce Type: cross Abstract: Silicon-photonic (SiPh) accelerators have emerged as a promising platform for Vision Transformer (ViT) inference by performing matrix multiplications on microring-resonator (MRR) banks with high throughput and energy efficiency.

By Xuming Chen, Deniz Najafi, Mehrdad Morsali, Chengwei Zhou, Zahra Ghanaatianjobzari, Mahdi Nikdast, Shaahin Angizi, Gourav Datta

llms computer-vision fine-tuning efficiency benchmarks

arXiv Machine Learning

Jun 18

Trainable Photonic Measurement for Physics-Informed PDE Learning

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By Jiale Linghu, Hao Dong, Yangshuai Wang

benchmarks

arXiv AI

Jul 7

Neural-Network Inverse Design of SRF Cavities and Transmons for Bosonic Quantum Computation

arXiv:2607. 02289v1 Announce Type: cross Abstract: Three-dimensional superconducting radio-frequency (SRF) cavities provide exceptionally long-lived electromagnetic modes and, when coupled to nonlinear elements such as transmon qubits, become promising architectures for bosonic quantum information processing.

By Joseph Yaker, Jovan Markovic, Alessandro Reineri, Doga Murat Kurkcuoglu, Silvia Zorzetti

arXiv AI

Jul 29

MDTransformer: A Hardware-Software Co-Design of Mode-Division Photonic Transformer Accelerator with Inverse-Designed Coherent Crossbar

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By Solomon Micheal Serunjogi, Rachmad Vidya Wicaksana Putra, Ayat Taha, Muhammad Shafique, Mahmoud Rasras

llms benchmarks

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