1 citations · 3 across the 5 of their papers we have counts for
5 papers
Optical Neural Engine for Solving Scientific Partial Differential Equations
Yingheng Tang, Ruiyang Chen, Minhan Lou +5
Solving partial differential equations (PDEs) is the cornerstone of scientific research and development. Data-driven machine learning (ML) approaches are emerging to accelerate tim…
Perfectly Perform Machine Learning Task with Imperfect Optical Hardware Accelerator
Jichao Fan, Yingheng Tang, Weilu Gao
Optical architectures have been emerging as an energy-efficient and high-throughput hardware platform to accelerate computationally intensive general matrix-matrix multiplications…
Physics-aware Complex-valued Adversarial Machine Learning in Reconfigurable Diffractive All-optical Neural Network
Ruiyang Chen, Yingjie Li, Minhan Lou +5
Diffractive optical neural networks have shown promising advantages over electronic circuits for accelerating modern machine learning (ML) algorithms. However, it is challenging to…
Wafer-scale, full-coverage, acoustic self-limiting assembly of particles on flexible substrates
Liang Zhao, Bchara Sidnawi, Jichao Fan +6
Self-limiting assembly of particles represents the state-of-the-art controllability in nanomanufacturing processes where the assembly stops at a designated stage1,2, providing a de…
Physics-Guided and Physics-Explainable Recurrent Neural Network for Time Dynamics in Optical Resonances
Yingheng Tang, Jichao Fan, Xinwei Li +4
Understanding the time evolution of physical systems is crucial to revealing fundamental characteristics that are hidden in frequency domain. In optical science, high-quality reson…