Article in Science advances, 2025. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Cited by 1 paper.
0numbers the graph read from it
0cells of the map it votes in
1citing papers in PubMed
–field-weighted citation impact
1 · What the graph read from it
What it found
Each row is one number read from the abstract, on the scale the paper reported it, with its interval. Left of the dashed line favours the treatment, right favours the comparator. Under each row is the sentence it came from. New to these charts? A ten-minute tutorial.
The abstract states no effect estimate the extractor could read, or names no intervention and outcome on the map, so this paper lights no cell and moves no belief. It is still indexed, cited and linked below.
2 · The registry
The trial behind it
Trials whose registry record cites this paper, or whose number appears in the abstract. A trial that started after this paper was published is citing it as background, not reporting it.
Neither the registry nor the abstract names a trial number. If this is a trial report, that itself is worth knowing.
PubMed lists nothing against this paper. Absence here is not a guarantee, only a check that was made.
5 · Who and what money
Authors and funding
26 authors.
Hegan ChenDepartment of Electrical and Electronic Engineering, the University of Hong Kong, Hong Kong, China.ORCID 0000-0002-9661-2727
Jichang YangDepartment of Electrical and Electronic Engineering, the University of Hong Kong, Hong Kong, China.ORCID 0000-0003-3760-6762
Jia ChenDepartment of Electronic and Computer Engineering, the Hong Kong University of Science and Technology, Hong Kong, China.ORCID 0000-0003-4814-5420
Songqi WangDepartment of Electrical and Electronic Engineering, the University of Hong Kong, Hong Kong, China.ORCID 0009-0001-3771-8150
Shaocong WangDepartment of Electrical and Electronic Engineering, the University of Hong Kong, Hong Kong, China.ORCID 0000-0002-7195-8676
Dingchen WangDepartment of Electrical and Electronic Engineering, the University of Hong Kong, Hong Kong, China.ORCID 0000-0003-3747-1626
Xinyu TianDepartment of Electrical and Electronic Engineering, the University of Hong Kong, Hong Kong, China.ORCID 0009-0009-1522-5951
Yifei YuDepartment of Electrical and Electronic Engineering, the University of Hong Kong, Hong Kong, China.ORCID 0009-0003-9208-2903
Xi ChenDepartment of Electrical and Electronic Engineering, the University of Hong Kong, Hong Kong, China.
Yinan LinDepartment of Electrical and Electronic Engineering, the University of Hong Kong, Hong Kong, China.
Qifan ZhuDepartment of Electrical and Electronic Engineering, the University of Hong Kong, Hong Kong, China.
Yangu HeDepartment of Electrical and Electronic Engineering, the University of Hong Kong, Hong Kong, China.ORCID 0009-0008-9035-1686
Xiaoshan WuDepartment of Electrical and Electronic Engineering, the University of Hong Kong, Hong Kong, China.
Yi LiDepartment of Electrical and Electronic Engineering, the University of Hong Kong, Hong Kong, China.ORCID 0000-0001-8061-6099
Xinyuan ZhangDepartment of Electrical and Electronic Engineering, the University of Hong Kong, Hong Kong, China.
Ning LinDepartment of Electrical and Electronic Engineering, the University of Hong Kong, Hong Kong, China.ORCID 0000-0002-6198-7043
Meng XuDepartment of Electrical and Electronic Engineering, the University of Hong Kong, Hong Kong, China.ORCID 0000-0003-1674-6037
Yi LiSchool of Integrated Circuits, Hubei Key Laboratory for Advanced Memories, Huazhong University of Science and Technology, Wuhan 430074, China.ORCID 0000-0003-1930-8550
Xumeng ZhangState Key Laboratory of Integrated Chips and Systems, Frontier Institute of Chip and System, Fudan University, Shanghai 200433, China.ORCID 0000-0002-3828-151X
Xiaojuan QiDepartment of Electrical and Electronic Engineering, the University of Hong Kong, Hong Kong, China.ORCID 0000-0002-4285-1626
Zhongrui WangDepartment of Electrical and Electronic Engineering, the University of Hong Kong, Hong Kong, China.ORCID 0000-0003-2264-0677
Han WangDepartment of Electrical and Electronic Engineering, the University of Hong Kong, Hong Kong, China.ORCID 0000-0001-5121-3362
Dashan ShangState Key Lab of Fabrication Technologies for Integrated Circuits and Key Laboratory of Microelectronic Devices & Integrated Technology, Institute of Microelectronics, Chinese Academy of Sciences, Beijing 100029, China.ORCID 0000-0003-3573-8390
Qi LiuState Key Laboratory of Integrated Chips and Systems, Frontier Institute of Chip and System, Fudan University, Shanghai 200433, China.ORCID 0000-0001-7062-831X
Kwang-Ting ChengDepartment of Electronic and Computer Engineering, the Hong Kong University of Science and Technology, Hong Kong, China.ORCID 0000-0002-3885-4912
Ming LiuState Key Lab of Fabrication Technologies for Integrated Circuits and Key Laboratory of Microelectronic Devices & Integrated Technology, Institute of Microelectronics, Chinese Academy of Sciences, Beijing 100029, China.ORCID 0000-0002-0937-7547
Funding
No grant is acknowledged in the PubMed record.
6 · The paper itself
Abstract
Digital twins, which replicate real-world entities through computational models, are transforming manufacturing and automation. While recent advances in machine learning have enabled data-driven digital twin development using discrete-time data and finite-depth models on digital hardware, these approaches face significant limitations. They struggle to capture continuous-time dynamics and model complex systems, and suffer from substantial time and energy overheads due to physically separated storage and processing as well as frequent analog-digital (A/D) conversions. Here, we propose a memristive neural ordinary differential equation (ODE) solver for digital twins. Our approach is intrinsically time-continuous using infinite-depth neural networks to model complex dynamics. Fully analog memristor arrays collocate storage and computation, addressing the von Neumann bottleneck and reducing A/D conversion requirements. We experimentally validate our solver on digital twins of HP variable-resistor model and Lorenz96 dynamics, demonstrating a 166.5-fold/369.3-fold speedup and a 499.0-fold/673.9-fold improvement in energy efficiency, respectively. This work paves the way to future digital twins for Industry 4.0.
Read under generation 80e0d062 · epoch 390. Bibliography from PubMed, PubMed Central and OpenAlex; grants from NIH RePORTER; trial links from ClinicalTrials.gov; estimates, votes and beliefs from the OpenQuestion graph.
Continuous-time digital twin with analog memristive neural ordinary differential equation solver. · full record | OpenQuestion