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Analysis of the Coupling Effects of Built and Environmental Factors on Researchers’ Psychological Satisfaction with University Research Spaces Based on the Sparse Identification of Nonlinear Dynamics (SINDy) Algorithm

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High levels of occupational stress are common among university researchers, creating increasingly stringent requirements for indoor environmental quality (IEQ). However, existing evaluation models tend to be built for conventional office or residential environments and typically assume linear relationships, making them insufficient for capturing the nonlinear coupling effects among multiple environmental variables. Previous evaluation models struggle to capture the nonlinear coupling relationships among multiple environmental factors, failing to establish a comprehensive psychological satisfaction evaluation model for university research spaces. This study focuses on the psychological health of university researchers and, taking the Shandong region as a case study, investigates how the built factors and environmental factors of research spaces (e.g., lighting, noise, layout form) influence researchers’ psychological states. A combined weighting method based on Principal Component Analysis and the Analytic Hierarchy Process (PCA-AHP) was employed to extract seven key environmental factors. A total of 1347 subjective and objective datasets were collected under 18 experimental conditions. The present study innovatively introduces the Sparse Identification of Nonlinear Dynamics (SINDy) algorithm from nonlinear dynamical systems theory to establish a psychological satisfaction evaluation model capable of capturing the coupling effects among multiple environmental factors. The results indicate that participants’ prior emotional states significantly affect psychological perception and evaluation. Significant coupling effects were observed between indoor temperature and per capita area, as well as between temperature and distance from the window (dist_window); with increasing temperature, the positive contributions of per capita area and dist_window to psychological satisfaction are attenuated. This study reveals the coupled relationships among multiple environmental factor

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