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Continuous-time digital twin with analog memristive neural ordinary differential equation solver
Hegan Chen1,2,3, Jichang Yang1,3, Jia Chen4
1Department of Electrical and Electronic Engineering, the University of Hong Kong, Hong Kong, China.
Science Advances
|May 28, 2025
Summary
This study introduces a novel memristive neural ordinary differential equation (ODE) solver for advanced digital twins. This innovation significantly enhances speed and energy efficiency for Industry 4.0 applications.
Area of Science:
- Computational Science
- Materials Science
- Artificial Intelligence
Background:
- Digital twins are crucial for manufacturing and automation, but current machine learning approaches struggle with continuous-time dynamics and energy efficiency.
- Existing methods face limitations due to discrete-time data, finite-depth models, and the von Neumann bottleneck, involving separate storage and processing with analog-digital conversions.
Purpose of the Study:
- To develop a time-continuous digital twin solver using memristive neural ordinary differential equations (ODEs).
- To overcome the limitations of current digital twin technologies by integrating computation and memory.
Main Methods:
- Proposed a memristive neural ODE solver utilizing infinite-depth neural networks for continuous-time dynamics.
- Employed fully analog memristor arrays to collocate storage and computation, mitigating the von Neumann bottleneck and reducing analog-digital conversion needs.
Main Results:
- Experimental validation on digital twins of HP variable-resistor model and Lorenz96 dynamics.
- Achieved significant speedups (166.5-fold/369.3-fold) and energy efficiency improvements (499.0-fold/673.9-fold).
Conclusions:
- The memristive neural ODE solver offers a pathway to more efficient and capable digital twins.
- This advancement is poised to enable future digital twins for Industry 4.0, enhancing real-world system modeling and simulation.
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