Analytical tools derived from nonlinear dynamics and dynamical systems theory, such as phase-space reconstruction and Recurrence Quantification Analysis (RQA), provide a powerful framework for investigating complex systems across different scientific domains. These methods allow the identification of dynamical structures, including recurrence, nonlinearity, and transitions between states, in time series data originating from diverse contexts. Scientific research is often shaped by two opposing forces that resemble the dynamics of physics: a centrifugal force, associated with increasing specialization, and a centripetal force, associated with interdisciplinarity. The rapid development of technologies and analytical methods has led to highly specialized languages and frameworks, which, while enabling scientific progress, can also generate fragmentation and communication barriers between disciplines. In contrast, interdisciplinarity emerges as a centripetal force that promotes the identification of shared analytical frameworks across domains. In this context, the transfer of methods is not merely a consequence of mathematical convenience but reflects the presence of common dynamical properties governed by similar physical principles. Artificial intelligence, integrated within physics-informed computational frameworks, provides a powerful tool for analyzing complex, high-dimensional, and heterogeneous datasets while preserving the dynamical structure of the underlying system. This convergence is not merely technical: the same nonlinear dynamical principles that govern physiological and cognitive systems appear to operate within artificial ones, suggesting that AI is not external to the phenomena this manuscript addresses but continuous with them. This inherent interdisciplinarity positions AI as a centripetal force, drawing together methods, languages, and findings from otherwise distant disciplines around a shared dynamical core
Interdisciplinarity as a centripetal force: physics-based methods for complex systems to artificial
Giovanna Zimatore
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2026-01-01
Abstract
Analytical tools derived from nonlinear dynamics and dynamical systems theory, such as phase-space reconstruction and Recurrence Quantification Analysis (RQA), provide a powerful framework for investigating complex systems across different scientific domains. These methods allow the identification of dynamical structures, including recurrence, nonlinearity, and transitions between states, in time series data originating from diverse contexts. Scientific research is often shaped by two opposing forces that resemble the dynamics of physics: a centrifugal force, associated with increasing specialization, and a centripetal force, associated with interdisciplinarity. The rapid development of technologies and analytical methods has led to highly specialized languages and frameworks, which, while enabling scientific progress, can also generate fragmentation and communication barriers between disciplines. In contrast, interdisciplinarity emerges as a centripetal force that promotes the identification of shared analytical frameworks across domains. In this context, the transfer of methods is not merely a consequence of mathematical convenience but reflects the presence of common dynamical properties governed by similar physical principles. Artificial intelligence, integrated within physics-informed computational frameworks, provides a powerful tool for analyzing complex, high-dimensional, and heterogeneous datasets while preserving the dynamical structure of the underlying system. This convergence is not merely technical: the same nonlinear dynamical principles that govern physiological and cognitive systems appear to operate within artificial ones, suggesting that AI is not external to the phenomena this manuscript addresses but continuous with them. This inherent interdisciplinarity positions AI as a centripetal force, drawing together methods, languages, and findings from otherwise distant disciplines around a shared dynamical coreI documenti in IRIS sono protetti da copyright e tutti i diritti sono riservati, salvo diversa indicazione.
