RESEARCH ARTICLE

Physics-informed neuromorphic learning: Enabling scalable industrial digital twins

¹ School of Automation and Intelligent Sensing, Shanghai Jiao Tong University, Shanghai 200240, China
² Key Laboratory for System Control and Information Processing, Ministry of Education, Shanghai 200240, China
³ Shanghai Key Laboratory for Perception and Control in Industrial Network Systems, Shanghai 200240, China
⁴ The Department of Electrical and Electronic Engineering, University of Manchester, Manchester M13 9PL, UK
⁵ SJTU-Paris Elite Institute of Technology, Shanghai Jiao Tong University, Shanghai 200240, China
⁶ Shanghai Key Laboratory of Integrated Administration Technologies for Information Security, Shanghai 200240, China

^* Corresponding author (emails: cailianchen@sjtu.edu.cn (Cailian Chen); xpguan@sjtu.edu.cn (Xinping Guan))

Received: 24 April 2025
Revised: 8 July 2025
Accepted: 30 July 2025

Abstract

Digital twins (DTs) have shown promise in industrial automation by facilitating seamless fusion between virtual and physical spaces, thereby enhancing operational efficiency. However, existing DTs are predominantly customized implementations, requiring advanced expertise and significant resources, which hinders their broader application. Here, we propose a physics-informed neuromorphic learning framework, a computationally efficient approach based on the brain-inspired structural representation. This physics-guided representation, inferred and integrated through prior cross-modal structural knowledge, provides a scalable foundation for DT construction with brain-like generalization capabilities and ultra-low computational complexity. We demonstrate its effectiveness across multiple industrial scenarios, attaining accuracy comparable to current DT modeling techniques while reducing computational latency to less than 1/30. This framework shifts the paradigm from complex, customized DTs to a simplified, unified solution, potentially saving considerable human and computational resources. By facilitating the practical integration of DTs into industrial workflows, our method marks a substantial advancement in accelerating industrial transformation.