AUTHOR=Xie Qiuen , He Min , Lu Zhang 

TITLE=Application of computer vision based nonlinear physical system dynamic behavior analysis in education

JOURNAL=Frontiers in Physics

VOLUME=Volume 13 - 2025

YEAR=2025

URL=https://www.frontiersin.org/journals/physics/articles/10.3389/fphy.2025.1556601

DOI=10.3389/fphy.2025.1556601

ISSN=2296-424X

ABSTRACT=IntroductionThe dynamic behavior analysis of nonlinear physical systems plays a critical role in understanding complex processes across various domains, including education, where interactive simulations of such systems can enhance conceptual learning. Traditional modeling techniques for nonlinear systems often fail to capture their high-dimensional, multi-scale, and chaotic nature due to oversimplified assumptions or reliance on linear approximations.MethodsIn this study, we present a novel framework leveraging computer vision and advanced neural architectures to analyze the dynamic behaviors of nonlinear physical systems. The proposed Physics-Informed Nonlinear Dynamics Network (PNDN) integrates data-driven embeddings with physics-based constraints, offering a robust solution for capturing intricate dynamics and ensuring adherence to physical principles.ResultsExperimental results highlight the model’s superior performance in reconstructing and predicting nonlinear system behaviors under diverse conditions, establishing its utility for real-time educational simulations.DiscussionThis approach bridges the gap between computational modeling and educational innovation, providing learners with interactive tools to explore complex physical phenomena.