Siqi Peng

Papers

Total Citations

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About

Siqi Peng is an emerging researcher in aerospace engineering, whose work bridges deep learning, contact mechanics, and space debris mitigation. Their most-cited paper, “Hybrid model of deep learning and contact theory for predicting distributed contact force in space debris de-tumbling” (2025), introduces a novel framework that fuses neural networks with classical contact theory to accurately forecast contact forces during the de-tumbling of uncontrolled space debris. This hybrid approach addresses a critical challenge in active debris removal—enabling safer, more efficient capture and stabilization of defunct satellites. With 3 citations already in its first year, the work signals growing interest in Peng’s innovative methodology. By combining data-driven learning with physics-based modeling, Peng offers a scalable solution for real-time force prediction, which is essential for robotic manipulators and net-based capture systems. Their research stands at the intersection of artificial intelligence and space sustainability, contributing to the broader goal of reducing orbital clutter. As the field of space debris remediation accelerates, Peng’s hybrid modeling technique provides a promising pathway for enhancing the precision and reliability of de-tumbling operations, marking them as a researcher to watch in this critical domain.