Robert Rai | Searcharxiv

arXiv2026

Effect of Colored Noise on Coupled Thermoacoustic Oscillators

Noise can significantly influence thermoacoustic dynamics, yet the role of noise color in coupled thermoacoustic oscillators remains largely unexplored. Here, we examine the influence of colored noise on the dynamics of coupled thermoacoustic systems. The system consists of two coupled Rijke tube oscillators with time-delay and dissipative coupling. Stochastic forcing is modeled as an additive Ornstein-Uhlenbeck (OU) process, such that white and colored noise contain equal power within a band around the system's natural frequency. We find that noise influences the system most prominently near the transition between limit-cycle oscillations (LCO) and amplitude death (AD) states, where increasing noise amplitude smoothen the transition and reduces the extent of the AD regions. Our analysis reveals the emergence of coherence resonance near instability threshold under both white and colored noise. The peak coherence factor varies with the noise color, with the largest peak coherence observed for colored noise whose correlation time is much shorter than the acoustic time scale. White noise and the shortest-correlated OU noise exert the strongest influence on both the pressure amplitude response and the coherence resonance. Overall, our results show that, under both coupling mechanisms, colored noise induces qualitatively similar trends in the system response, governed by its amplitude and correlation time.

nlin.CD↗

arXiv2025

IPSR Model: Misinformation Intervention through Prebunking in Social Systems

The rapid dissemination of misinformation through online social networks poses a growing threat to public understanding and societal stability. Prebunking, a proactive strategy based on inoculation theory, has recently emerged as an effective intervention to build cognitive resilience against misinformation before exposure. In this work, we investigate the impact of prebunking on misinformation dynamics using a compartmental modeling framework. We first analyze the classical Ignorant-Spreader-Stifler (ISR) model, its parameters are determined using empirical rumor data from Twitter. We then propose an extended model, the Ignorant-Prebunked-Spreader-Stifler (IPSR) model, which incorporates prebunking as a preventive state and includes a forgetting mechanism to account for the decay of cognitive immunity over time. Using mean-field approximations, we derive steady-state solutions and examine the effect of prebunking on the spreading of misinformation. We further investigate the robustness of the IPSR model by varying network size and average degree. In addition, we analyze the model's behavior on Watts-Strogatz and Barabasi-Albert networks to assess the role of small-world and scale-free structures in shaping intervention outcomes. Our results show that the inclusion of prebunking significantly reduces the scale of misinformation outbreaks across different network structures. These findings highlight the efficacy of prebunking as a scalable intervention strategy and underscore the utility of compartmental models in understanding and mitigating information-based contagion in complex networks.

physics.soc-ph↗