2 citations · 5 across the 13 of their papers we have counts for
13 papers
Dataset Distillation for Machine Learning Force Field in Phase Transition Regime
Ruiyang Chen, Qingyuan Zhang, Ji Chen
Machine learning force field (MLFF) has emerged as a powerful data-driven tool for atomistic simulations, enabling large-scale and complex atomic systems to be simulated with accur…
Optical modeling, solver, and design of wafer-scale single-enantiomer carbon nanotube film and reconfigurable chiral photonic device
Jichao Fan, Benjamin Hillam, Cheng Guo +6
The interaction of circularly polarized light with chiral matter and functional devices enables novel phenomena and applications. Recently, wafer-scale solid-state single-enantiome…
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…
Digitized Phase Change Material Heterostack for Diffractive Optical Neural Network
Ruiyang Chen, Cunxi Yu, Weilu Gao
All-optical and fully reconfigurable diffractive optical neural network (DONN) architectures are promising for high-throughput and energy-efficient machine learning (ML) hardware a…
A programmable wafer-scale chiroptical heterostructure of twisted aligned carbon nanotubes and phase change materials
Jichao Fan, Ruiyang Chen, Minhan Lou +4
The ability to design and dynamically control chiroptical responses in solid-state matter at wafer scale enables new opportunities in various areas. Here we present a full stack of…
LightRidge: An End-to-end Agile Design Framework for Diffractive Optical Neural Networks
Yingjie Li, Ruiyang Chen, Minhan Lou +3
To lower the barrier to diffractive optical neural networks (DONNs) design, exploration, and deployment, we propose LightRidge, the first end-to-end optical ML compilation framewor…
Scientific Computing with Diffractive Optical Neural Networks
Ruiyang Chen, Yingheng Tang, Jianzhu Ma +1
Diffractive optical neural networks (DONNs) have been emerging as a high-throughput and energy-efficient hardware platform to perform all-optical machine learning (ML) in machine v…
Physics-aware Differentiable Discrete Codesign for Diffractive Optical Neural Networks
Yingjie Li, Ruiyang Chen, Weilu Gao +1
Diffractive optical neural networks (DONNs) have attracted lots of attention as they bring significant advantages in terms of power efficiency, parallelism, and computational speed…
Device-system Co-design of Photonic Neuromorphic Processor using Reinforcement Learning
Yingheng Tang, Princess Tara Zamani, Ruiyang Chen +4
The incorporation of high-performance optoelectronic devices into photonic neuromorphic processors can substantially accelerate computationally intensive operations in machine lear…
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…
Real-time Multi-Task Diffractive Deep Neural Networks via Hardware-Software Co-design
Yingjie Li, Ruiyang Chen, Berardi Sensale Rodriguez +2
Deep neural networks (DNNs) have substantial computational requirements, which greatly limit their performance in resource-constrained environments. Recently, there are increasing…
Artificial Intelligence Accelerators based on Graphene Optoelectronic Devices
Weilu Gao, Cunxi Yu, Ruiyang Chen
Optical and optoelectronic approaches of performing matrix-vector multiplication (MVM) operations have shown the great promise of accelerating machine learning (ML) algorithms with…