Yongshi Song

China University of Mining and Technology

Papers

Total Citations

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About

Yongshi Song is a researcher at the forefront of smart materials and soft robotics, with a focus on Ionic Polymer-Metal Composites (IPMCs) and magnetically actuated continuum systems. Their major contributions include a comprehensive 2025 review on IPMC fabrication, encapsulation, and hysteresis creep mitigation, which systematically evaluates methods to suppress the materials' inherent nonlinear behavior—a critical step toward reliable actuation in soft robotics. This work, with 3 citations, serves as a foundational resource for engineers seeking to enhance IPMC performance. In parallel, Song pioneered the use of a State-Dependent Switching Physics-Informed Neural Network (SDS-PINN) to predict the full-workspace deformation of magnetic soft continuum robots (MSCRs). This innovative approach, detailed in a 2025 paper with 1 citation, addresses the complex, nonlinear dynamics of MSCRs in constrained environments like minimally invasive surgery, offering a data-driven solution for precise control. By bridging material science and machine learning, Song’s research advances the practical deployment of soft robots, with implications for medical devices and beyond. Their work exemplifies a commitment to solving real-world challenges through interdisciplinary innovation.