Continuous-time digital twin with analog memristive neural ordinary differential equation solver
- Hegan Chen
- Jichang Yang
- Jia Chen
- Songqi Wang
- Shaocong Wang
- Dingchen Wang
- Xinyu Tian
- Yifei Yu
- Xi Chen
- Yinan Lin
- Qifan Zhu
- Yangu He
- Xiaoshan Wu
- Yi Li
- Xinyuan Zhang
- Ning Lin
- Meng Xu
- Xumeng Zhang
- Xiaojuan Qi
- Zhongrui Wang
- Han Wang
- Dashan Shang
- Qi Liu
- Kwang-Ting Cheng
- Ming Liu
2025-05-28
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.